Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:11:57.069990Z
Paper Citation Record · LEDGER
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems
As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2506.22374.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
27 of 27 outbound references displayed
- verified exact0
- verified fuzzy25
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 86703339-a9de-4ff8-b77d-08110c7b0717 · outbound
This paper cites A vision of 6g wireless systems: Applications, trends, technologies, and open research problems,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems A vision of 6g wireless systems: Applications, trends, technologies, and open research problems,
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:11:53.607904Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1077383-0064-40c7-a1ab-add8d99e42c8 · outbound
This paper cites Proactively predicting dynamic 6G link blockages using LiDAR and in-band signatures,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Proactively predicting dynamic 6G link blockages using LiDAR and in-band signatures,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:01.795970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a870b36d-f15e-4446-ab8a-59e98fd26668 · outbound
This paper cites Proactive received power pre- diction using machine learning and depth images for mmwave networks,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Proactive received power pre- diction using machine learning and depth images for mmwave networks,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:01.585046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0e901f19-da96-4d2e-8ac1-d650b50306fe · outbound
This paper cites Millimeter-wave networking in the sky: A machine learning and mean field game approach for joint beamforming and beam-steering,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Millimeter-wave networking in the sky: A machine learning and mean field game approach for joint beamforming and beam-steering,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:01.405129Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3428f642-f939-4f9a-b014-325bf8adb154 · outbound
This paper cites Hybrid beamforming/combining for millimeter wave mimo: A machine learning approach,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Hybrid beamforming/combining for millimeter wave mimo: A machine learning approach,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:01.169989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b35d4833-729f-4359-ab67-2192e6fef779 · outbound
This paper cites Towards real-world 6g drone communication: Position and camera aided beam prediction,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Towards real-world 6g drone communication: Position and camera aided beam prediction,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:01.016585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation eb3b48e1-50b8-4d36-808c-913af4b61ebf · outbound
This paper cites Deep multimodal learning: Merging sensory data for massive mimo channel prediction,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Deep multimodal learning: Merging sensory data for massive mimo channel prediction,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:00.848921Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 97d6e422-fc85-49e7-9447-aa04acc46bcd · outbound
This paper cites Din: A decentral- ized inexact newton algorithm for consensus optimization,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Din: A decentral- ized inexact newton algorithm for consensus optimization,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:00.659113Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0f8d8404-a9df-489c-8694-8288ec5b2d90 · outbound
This paper cites Scalable and resource- efficient second-order federated learning via over-the-air aggregation,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Scalable and resource- efficient second-order federated learning via over-the-air aggregation,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:00.467754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation bde367ea-d991-4952-8647-13da133fcb1c · outbound
This paper cites Distributed machine learning based downlink channel estimation for ris assisted wireless communications,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Distributed machine learning based downlink channel estimation for ris assisted wireless communications,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:00.326685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e1fa7987-5b18-47b9-9e26-3fd88b19a06c · outbound
This paper cites Federated learning for wireless communications: Motivation, opportunities, and challenges,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Federated learning for wireless communications: Motivation, opportunities, and challenges,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:12:00.115029Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 41772d2c-9c58-49a2-a1f9-027c5476749a · outbound
This paper cites Distributed machine learning for uav swarms: Computing, sensing, and semantics,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Distributed machine learning for uav swarms: Computing, sensing, and semantics,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:59.913802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e1067e84-1e2d-4255-8993-2226da1a7d2a · outbound
This paper cites Tackling modality- heterogeneous client drift holistically for heterogeneous multimodal fed- erated learning,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Tackling modality- heterogeneous client drift holistically for heterogeneous multimodal fed- erated learning,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:59.724614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c2c8e109-f7ae-4a7e-9786-6856c72cdb45 · outbound
This paper cites Multi-modality sensing in mmwave beamforming for connected vehicles using deep learning,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Multi-modality sensing in mmwave beamforming for connected vehicles using deep learning,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:59.563765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9d140b0c-3d2d-4091-b285-91ee7b64cf3f · outbound
This paper cites Wireless interference recognition with multimodal learning,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Wireless interference recognition with multimodal learning,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:59.401981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2c823a7f-c63b-4a60-83e8-36041cbab6bd · outbound
This paper cites Multimodal Federated Learning via Contrastive Representation Ensemble.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Multimodal Federated Learning via Contrastive Representation Ensemble
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:11:55.203937Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f00ed2ef-8742-4ecf-9a49-e025faf30aac · outbound
This paper cites Harmony: Heterogeneous multi-modal federated learning through disentangled model training,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Harmony: Heterogeneous multi-modal federated learning through disentangled model training,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:59.229529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 699cd657-f92e-40dd-9316-a99d1fb37979 · outbound
This paper cites Towards optimal multi-modal federated learning on non-iid data with hierarchical gradient blending,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Towards optimal multi-modal federated learning on non-iid data with hierarchical gradient blending,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:59.045972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f4fcbf22-78c9-457f-971d-9325bde15ffa · outbound
This paper cites Fedmsplit: Correlation-adaptive federated multi- task learning across multimodal split networks,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Fedmsplit: Correlation-adaptive federated multi- task learning across multimodal split networks,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:58.880543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cb86b195-fde6-4ce1-958b-8948299508d5 · outbound
This paper cites Tackling feature and sample heterogeneity in decentralized multi-task learning: A sheaf- theoretic approach,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Tackling feature and sample heterogeneity in decentralized multi-task learning: A sheaf- theoretic approach,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:58.732419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 848b98f9-b976-4735-86ca-bfbfd1121499 · outbound
This paper cites Robinson, Topological signal processing.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Robinson, Topological signal processing
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:58.580731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e489adf7-693d-4a26-af91-ff6f3c9cbacf · outbound
This paper cites A sheaf-theoretic ap- proach to decentralized multimodal federated learning for next-generation communication systems,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems A sheaf-theoretic ap- proach to decentralized multimodal federated learning for next-generation communication systems,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:58.429031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1a39845a-1097-4ab5-bc26-f6436a61145e · outbound
This paper cites Federated multi-task learning,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Federated multi-task learning,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:58.205302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dd34b0d6-606a-49db-b2f7-e7ba8081cabb · outbound
This paper cites Exploiting shared representations for personalized federated learning,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Exploiting shared representations for personalized federated learning,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:58.040278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3360323a-0e1b-4245-821c-e024384fd82b · outbound
This paper cites Distributed learning over networks with graph-attention-based personalization,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Distributed learning over networks with graph-attention-based personalization,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:57.888571Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e7978396-ff21-4b2f-9e8d-f0c1c6ea3e07 · outbound
This paper cites Attention is all you need,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Attention is all you need,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:57.700255Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0249a6e4-5404-43d4-bb80-a94e1e463c67 · outbound
This paper cites Knowledge distillation and training bal- ance for heterogeneous decentralized multi-modal learning over wireless networks,.
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems Knowledge distillation and training bal- ance for heterogeneous decentralized multi-modal learning over wireless networks,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T22:11:57.402832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.