Paper Citation Record · LEDGER

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2108.11887.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2108.11887 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:56:29.518014Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T13:33:28.488039Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 917dc54c-3aa9-43b0-819c-943a25dd2e94 · inbound

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters cites this paper.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:56:29.518014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42fdfb50-c1f4-43a3-a969-c3d808803428 · inbound

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes cites this paper.

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T19:19:11.947862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49c703eb-fb1c-40a6-8848-f03bdff75082 · inbound

Federated Reinforcement Learning in Heterogeneous Environments cites this paper.

Federated Reinforcement Learning in Heterogeneous Environments Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:12:20.480072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2d22a59-4d3a-4b8e-9b54-c3c34a066e47 · inbound

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks cites this paper.

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T18:09:29.057515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34c1ef7d-dc31-4b06-aff2-448dcfcd4287 · inbound

Scalar Federated Learning for Linear Quadratic Regulator cites this paper.

Scalar Federated Learning for Linear Quadratic Regulator Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:30:52.429777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80194a04-08b0-4e0e-bd16-20a513348340 · inbound

Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments cites this paper.

Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:00:34.797514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9baef010-f8dd-4335-b67a-7b92e5dff269 · inbound

Insider Attacks in Multi-Agent LLM Consensus Systems cites this paper.

Insider Attacks in Multi-Agent LLM Consensus Systems Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 223

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:23.775900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 35984cec-39d0-4890-b1f6-3730166cda9f · inbound

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems cites this paper.

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:51:40.758041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd38a438-7177-48a5-a1c2-fc7c033abc6f · inbound

Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity cites this paper.

Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T23:02:52.079919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 768d434e-6465-4261-a776-f14d289996aa · inbound

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning cites this paper.

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:33:28.489624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.