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Paper Citation Record · LEDGER

Federated Deep Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1901.08277.

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

pith.paper-citation-record.v1
1901.08277 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:19:12.026037Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T13:33:28.484578Z

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 1c78533d-70de-4513-a341-2de3cc825b1f · 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 Deep Reinforcement Learning

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-06T19:19:12.026037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:19:12.026037Z digest=sha256:abf4c924bcf5f4f24ef1597682da5a131e5acd853ce055012bf6be8bae9e421f

Observation fe59c430-8b08-4c93-b20c-cb26c76a5ee0 · inbound

Federated Reinforcement Learning in Heterogeneous Environments cites this paper.

Federated Reinforcement Learning in Heterogeneous Environments Federated Deep Reinforcement Learning

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T16:12:23.104452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:12:23.104452Z digest=sha256:a69a73eaf859c8be8c9c0288f62608873461304cf6cd170b9faf1b60d56de1b6

Observation 7a702df2-0e41-4aea-ba6c-db6575946f68 · inbound

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints cites this paper.

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints Federated Deep Reinforcement Learning

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:36:04.592613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T01:27:49.203891Z digest=sha256:7c9f0979c5c3d0b4cf599377c0e8a4d5349a9164b6527d324b4cf3c9ca16d726

Observation e2dc22ca-2cf5-4571-8563-af9c559cb787 · inbound

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints cites this paper.

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints Federated Deep Reinforcement Learning

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-20T23:49:15.073300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T23:47:34.192789Z digest=sha256:b65ef2251f0fa47f17ddad688980fc7e7b4daf07df980fc4b26620934da80d93

Observation b947b011-37c0-4909-92b6-bdd215d8e0d2 · inbound

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

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems Federated Deep Reinforcement Learning

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:51:38.779693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T01:47:40.772146Z digest=sha256:8afde48828752d4a78589968874e5ec7cb1218260401512f383b76fb20d0a97c

Observation c1bcb7a4-4a10-48a8-8ef6-870d6229ab84 · inbound

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

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning Federated Deep Reinforcement Learning

Reference 10

Resolution
malformed identifier
arxiv_id, observed 2026-06-29T13:33:28.485913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T13:24:31.382577Z digest=sha256:62b4dd8fd1d99b91b33b8622dfb9be6a88d965a49680c13a370c28e3a200be19