Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.08003.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:42:06.565017Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T19:08:54.323876Z
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 f3e1da26-9abc-4a7b-9baa-d46e302e67e8 · inbound
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations cites this paper.
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:42:06.565017Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65ef5c1f-4439-4c08-af9d-5db04eaef724 · inbound
Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration cites this paper.
Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-17T01:53:51.039736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9cdfcf5a-5b13-41ff-8ccc-2fc865e40bc0 · inbound
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models cites this paper.
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Reference 72
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:08:54.326466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.