{"as_of":"2026-08-10T19:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1183ef067f0030be6e5704ad2e88f77c1497a3726c56a37f88bb03dd26448eab","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T18:09:31.660857Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.10163/citation-record","integrity":"/paper/2509.10163/integrity","json":"/paper/2509.10163/citation-record.json","paper":"/paper/2509.10163"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.121677Z","title":"6g wireless networks: Vision, requirements, architecture, and key technologies,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.121677Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:e6f1c7cc1db4d69af41020516099b438e9597e9356fff3f00d38b7db62f667bd","observation_id":"66f102a8-445d-4329-910b-f41bedc5be07","resolution":{"observed_at":"2026-08-04T18:09:28.121677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.167714Z","title":"Efficient multi-user computation offloading for mobile-edge cloud computing,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.167714Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:5570e101eb19cea0eeb4455c6fa62848c6487e14e093502fea5be2c65762a20f","observation_id":"8da62310-9374-4125-80af-ee8c8131f245","resolution":{"observed_at":"2026-08-04T18:09:28.167714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.296492Z","title":"Vehicular intelligence in 6g: Networking, communications, and computing,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.296492Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:4cd189e68ff0c853f265f08fec84fbb275749e6a68e5c2ce70882d8ea8a49e0d","observation_id":"87249283-15d0-4b45-8fe6-2ca102923a1d","resolution":{"observed_at":"2026-08-04T18:09:28.296492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.377078Z","title":"Decentralizing 6g security: Existing challenges and future opportunities,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.377078Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:7d34a13142f1342840de6ac4c34a6f48de9911c920bede71ea13c24ca2c3fe75","observation_id":"e57a6cfb-c97a-4cc3-a83b-051e6dc334fe","resolution":{"observed_at":"2026-08-04T18:09:28.377078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.494088Z","title":"What will 5g be?","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.494088Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:4629ad36cc6b405e79a635fc51fca9814ab1381ad7065cfde6ef2b1826e3ee89","observation_id":"f5afbb4f-6729-4cd5-a792-e7251e367f7c","resolution":{"observed_at":"2026-08-04T18:09:28.494088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05316","last_updated":"2019-10-04T00:53:44Z","snapshot_observed_at":"2026-07-06T08:28:51.112728Z","submitted_at":"2019-10-04T00:53:44Z","title":"Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","version":1},"cited_work":{"arxiv_id":"1910.05316","doi":"10.48550/arxiv.1910.05316","metadata_source":"pith","pith_arxiv_id":"1910.05316","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","venue":"cs.NI","work_id":"09df5531-9096-470c-b141-50d831160276","year":2019},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.631372Z"},"links":{"cited_paper":"/paper/1910.05316","citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:4d73ccab47a6b4a643489ef1b7830adc7758579fb3a49c55ceb6203c293ec008","observation_id":"8e6bc792-a442-4fce-b1b4-76ae869d8278","resolution":{"observed_at":"2026-08-04T18:14:12.119247Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.729907Z","title":"Mobility- aware caching and computation offloading in 5g ultra-dense cellular networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.729907Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:a633163f9ff0a86f9d34fedca9481a5f16e95ad39b32f416672c1ddf78861c6d","observation_id":"c133533c-0c77-40e6-8043-ae2b23278bfb","resolution":{"observed_at":"2026-08-04T18:09:28.729907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.825825Z","title":"Distributed deep reinforcement learning architecture for task offloading in au- tonomous iot systems,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.825825Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:1a25e6fcf90bf72518b19c44324b4bf01e21d2026b3f86ea617cfff1ebbadd6d","observation_id":"315f6d72-dd75-48a9-91b3-7325fa7da907","resolution":{"observed_at":"2026-08-04T18:09:28.825825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:28.954484Z","title":"Distributed deep multi- agent reinforcement learning for cooperative edge caching in internet- of-vehicles,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:28.954484Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:f89f4e68c649eba7cf7e97cf61396132a33a593818eaeb4775679a5da7bd672d","observation_id":"61494035-4016-4f31-a811-ba2ed0e20744","resolution":{"observed_at":"2026-08-04T18:09:28.954484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11887","last_updated":"2021-10-24T19:02:03Z","snapshot_observed_at":"2026-07-06T11:41:48.795143Z","submitted_at":"2021-08-26T16:22:49Z","title":"Federated Reinforcement Learning: Techniques, Applications, and Open Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.11887","snapshot_observed_at":"2026-08-04T18:09:29.057515Z","title":"Federated reinforcement learning: Techniques, applications, and open challenges,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.057515Z"},"links":{"cited_paper":"/paper/2108.11887","citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:6a33df7449409ebc508e638d8efb7864aca824560ce5d065b610adfa2cf01de7","observation_id":"e2d22a59-4d3a-4b8e-9b54-c3c34a066e47","resolution":{"observed_at":"2026-08-04T18:09:29.057515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.156338Z","title":"Co- operative multi-agent reinforcement-learning-based distributed dynamic spectrum access in cognitive radio networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.156338Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:dc712d913daeff15839ac1c7fd7903b8d3ba6d1391e0d529e8ba6891053904f8","observation_id":"3501403a-9085-4d63-9e88-1db6e899a892","resolution":{"observed_at":"2026-08-04T18:09:29.156338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.257797Z","title":"Scheduling of real-time wireless flows: A comparative study of centralized and decentralized reinforcement learning approaches,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.257797Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:fe5af6b7450337cf55883b260a3758bb3c3a4494ccce49108df0dbd917677499","observation_id":"f0cae155-3029-486b-969d-d5a997bb9665","resolution":{"observed_at":"2026-08-04T18:09:29.257797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.361493Z","title":"Centralized & distributed deep rein- forcement learning methods for downlink sum-rate optimization,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.361493Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:c95125a81a322c588fd98b67a4d973a292911b83994a47ac05c91dab0c0a022b","observation_id":"8dc1aa27-c720-4989-bb00-ea31bb3f5540","resolution":{"observed_at":"2026-08-04T18:09:29.361493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.463282Z","title":"Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.463282Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:953fc8f0dbc6f358fb150de476f58c31ee7ba77fd74f71ade1ecf15e9de3295e","observation_id":"4e1a5ddd-9e85-4b81-a586-a0cca95f5b95","resolution":{"observed_at":"2026-08-04T18:09:29.463282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.541928Z","title":"A multiagent reinforcement learning ap- proach considering fairness for multi-intersection traffic signal control,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.541928Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:309a3e43ed7171612e2bde377acdc61cb1ee08526984f386b4e64275e5f9f752","observation_id":"401edd0e-d67f-4803-90bc-ff75fa7f663f","resolution":{"observed_at":"2026-08-04T18:09:29.541928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.617462Z","title":"Deep reinforcement learning for mobile 5g and beyond: Fundamentals, applications, and challenges,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.617462Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:0e745359a33e011f9cf6364e3f6adc168dfcd518d7b11c61ce11cab5dea0cf4d","observation_id":"d06bc267-a495-4ca1-97e6-fd56a92c3812","resolution":{"observed_at":"2026-08-04T18:09:29.617462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.728672Z","title":"Communication-efficient learning of deep networks from decentralized data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.728672Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:476eb0794d5c9587b6e46025b95066518ffcbc5f79dc00e100421ed2b9ee06a0","observation_id":"ae0835f0-7a21-4d1b-94fd-4ba7d47ac654","resolution":{"observed_at":"2026-08-04T18:09:29.728672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.835792Z","title":"Advances and open problems in federated learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.835792Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:11d4a81d0dc0946f142c5b7bdb0102c879bda9ddbab675a242aefc17c08d4610","observation_id":"2c8a3990-da8b-470d-ad13-a9289b7741ea","resolution":{"observed_at":"2026-08-04T18:09:29.835792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:29.939051Z","title":"Federated learning for wireless communications: Motivation, opportunities and challenges,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:29.939051Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:c391ca9fd442f62f4b0ac55350f4020bcbef3b10b8ee7e91cd7e15180ac38166","observation_id":"582f3181-9cb4-492a-8f71-fd95ed1a1142","resolution":{"observed_at":"2026-08-04T18:09:29.939051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.051604Z","title":"A comprehensive study of gradient inversion attacks in federated learning and baseline defense strategies,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.051604Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:b6c7375a56af63e28ed0f91b3824ce2c3ae8a2680b6d6021e5b0a361c94b62aa","observation_id":"fc98c032-4ca8-4f7d-bdae-db4561c59e40","resolution":{"observed_at":"2026-08-04T18:09:30.051604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.121270Z","title":"Pp-marl: Efficient privacy-preserving multi-agent reinforcement learning for cooperative intelligence in com- munications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.121270Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:e5e85f4306c6bbe303a9f17a70b12dc0b606f3c553ae85f126ee078be648d5ae","observation_id":"95150087-6568-49e6-827e-dee3b32bab99","resolution":{"observed_at":"2026-08-04T18:09:30.121270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.221596Z","title":"Practical secure aggregation for privacy-preserving machine learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.221596Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:26795721b2d8a833797a71123b8538318ef9cf350aec15b2cd2bb6280a3553c6","observation_id":"4bc8d182-b655-40e2-9533-35626030b93e","resolution":{"observed_at":"2026-08-04T18:09:30.221596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12991","last_updated":"2025-01-22T16:25:46Z","snapshot_observed_at":"2026-08-10T16:30:58.340779Z","submitted_at":"2025-01-22T16:25:46Z","title":"An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12991","snapshot_observed_at":"2026-08-04T18:09:30.327529Z","title":"An offline multi-agent reinforcement learning framework for radio resource management,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.327529Z"},"links":{"cited_paper":"/paper/2501.12991","citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:10eda44842df5a0425aaa772422ff472a7b69618e18d94784765ba84cd0ae7b9","observation_id":"1d288dbb-6b10-4364-a3b6-13fe170d96bb","resolution":{"observed_at":"2026-08-04T18:09:30.327529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.396762Z","title":"Wireless resource allocation algorithm based on multi-objective deep reinforcement learning for vehicle-to-vehicle communications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.396762Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:b2df7d8e865b10b89284d8f5ac725db3377bb8beb180f095ebd7409b529b2d5d","observation_id":"1f67e255-bd63-4295-8828-aafa45af0c7b","resolution":{"observed_at":"2026-08-04T18:09:30.396762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.510621Z","title":"Exploring cross-layer techniques for security: Challenges and opportunities in wireless networks,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.510621Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:43a27a5b62566441bc05e4e3c6c5c6303df1eeeb679fe1d4a65b52125e4fff1c","observation_id":"969da2d1-6784-4cfa-a580-4338d9774d8d","resolution":{"observed_at":"2026-08-04T18:09:30.510621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.618108Z","title":"Multi-agent deep reinforcement learning for task offloading in vehicle edge computing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.618108Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:472f42fbec7e4cf936fc44aa201e8c2fcaeea7e4da28ad699d453baab4654c05","observation_id":"cccf8fa0-bc3c-4f6f-9cc6-dbc795096a09","resolution":{"observed_at":"2026-08-04T18:09:30.618108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.728626Z","title":"Energy-aware mobility management for mobile edge computing in ultra dense networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.728626Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:c9d3f13ec649b2df3b2d9d52d41433a804256b2d17c8e562024af2ba1dc70e51","observation_id":"c717a6f0-9f75-484b-b6c7-7ffb4bbd15e9","resolution":{"observed_at":"2026-08-04T18:09:30.728626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.830071Z","title":"A survey on resource allocation schemes in device-to-device communication,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.830071Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:bf549ef285674fb28370043c11433a2cc2f7774a744899a80e66e3a26fe947f2","observation_id":"637f4a03-9b96-4c3c-bc6a-5c60a7bcdb20","resolution":{"observed_at":"2026-08-04T18:09:30.830071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:30.951065Z","title":"5g orchestration and analysis: Dynamic approach, control and challenges faced,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:30.951065Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:fd741cfe950e1bdf7168c045bf9d34f6aded085bee1a379e434c324143f2eaa6","observation_id":"7f5b9b8d-0e33-4402-aca5-7114fc81a7ab","resolution":{"observed_at":"2026-08-04T18:09:30.951065Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.063735Z","title":"Multi-agent reinforcement learning for resource allocation in iot networks with edge computing,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.063735Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:49ba6aa2810f257c54071086fc342569af0b66ca5a2d1f77580ade7cee2288a9","observation_id":"a29ac5ed-b5f5-453a-9b73-10ec471957c1","resolution":{"observed_at":"2026-08-04T18:09:31.063735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.175261Z","title":"Fully decentralized multi-agent reinforcement learning with networked agents,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.175261Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:8fe1af5d5a79fae824f4adc4ccaad563e5054dd9990a1a011af10e5d2534f626","observation_id":"bfe10b74-8f2a-4856-9d31-8c254e5ba2fb","resolution":{"observed_at":"2026-08-04T18:09:31.175261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.296661Z","title":"An insight into federated learning: A collaborative approach for machine learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.296661Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:341171caa317ec96fed51878454c0b253f447c5e370ebd38b14a09b7256f58f9","observation_id":"dc4a3432-3843-4432-8bde-5ecf1ef162a5","resolution":{"observed_at":"2026-08-04T18:09:31.296661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.377464Z","title":"Federated learning in vehicular networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.377464Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:5b3666919241a94c7a2dcfef53aa0efc11ac28f12797660be2ba74c660a97d1c","observation_id":"4bdeb365-edd9-4ef0-8d98-45022b33862c","resolution":{"observed_at":"2026-08-04T18:09:31.377464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.489431Z","title":"Energy- aware selective inference task offloading for real-time edge computing applications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.489431Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:b363373fb060b6a01155e5ae6a34e01a7e49a1f044aebb31258896006e114788","observation_id":"bd86af6a-5616-44b4-99c7-7f60211e290b","resolution":{"observed_at":"2026-08-04T18:09:31.489431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.573274Z","title":"Reinforce- ment learning-based physical cross-layer security and privacy in 6g,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.573274Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:bcb6e16fcb714be2cf7ff9920c84ce1f9c3481097339bcc65db1dfd1b58eb670","observation_id":"c115c492-39ea-4803-b871-46f61a58c712","resolution":{"observed_at":"2026-08-04T18:09:31.573274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.578425Z","title":"Fauno: Semi- asynchronous federated reinforcement learning framework for task of- floading in edge systems,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.578425Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:95ab7dd2c3524e57bff4a6552621eebba3245ad656145ddcc1083afacd43c59f","observation_id":"338464b8-ff48-4250-b3ae-359a1e714295","resolution":{"observed_at":"2026-08-04T18:09:31.578425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.11320","last_updated":"2021-04-02T18:41:59Z","snapshot_observed_at":"2026-08-06T10:56:23.743235Z","submitted_at":"2021-04-02T18:41:59Z","title":"Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.11320","snapshot_observed_at":"2026-08-04T18:09:31.586362Z","title":"Federated double deep q-learning for joint delay and energy minimization in iot networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.586362Z"},"links":{"cited_paper":"/paper/2104.11320","citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:cbc44fc31c8ca2a7537377e89f6a6540f4901b9e6c80c1f2c52981af3ef045eb","observation_id":"da76572d-4137-491b-ab4d-67fba72b2538","resolution":{"observed_at":"2026-08-04T18:09:31.586362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.591989Z","title":"Fedrl-d2d: Federated deep reinforcement learning-empowered resource allocation scheme for energy efficiency maximization in d2d- assisted 6g networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.591989Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:b571c9c06363d0a9c588278627ef9ce73c821d0ec80ed7fc6f940483044bf1dd","observation_id":"8f22b734-cefb-40df-9f02-038280c0f799","resolution":{"observed_at":"2026-08-04T18:09:31.591989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T18:09:31.660857Z","title":"Optical wireless communications: Research challenges for mac layer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T18:09:31.660857Z"},"links":{"citing_paper":"/paper/2509.10163"},"observation_digest":"sha256:5f5616ac9da868e7ee826c26c2b37e4fe4cadec3f41e11d3f53f7683e03811fc","observation_id":"1d57db79-761b-4547-b65a-e24127e94a17","resolution":{"observed_at":"2026-08-04T18:09:31.660857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.10163","last_updated":"2025-09-12T11:41:40Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T09:44:43.801919Z","submitted_at":"2025-09-12T11:41:40Z","title":"Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":38,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":39},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2509.10163."}