Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.04845.
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, observed 2026-08-16T11:09:46.698293Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T22:23:47.940163Z
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 c8ac9e16-3bbd-4429-a5df-9be4c177fee0 · inbound
Symmetric Pruning of Large Language Models cites this paper.
Symmetric Pruning of Large Language Models FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T21:55:16.313725Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed2c2a7e-a1db-4b16-b242-10f27cc7b105 · inbound
POPri: Private Federated Learning using Preference-Optimized Synthetic Data cites this paper.
POPri: Private Federated Learning using Preference-Optimized Synthetic Data FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:09:46.698293Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 460ad1ab-f408-4bf7-a391-1ae2b390bd14 · inbound
Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.
Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Reference 228
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:06:26.591728Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98359982-fe4f-4242-b1e9-bac6d66b1cad · inbound
Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.
Concordia: Self-Improving Synthetic Tables for Federated LLMs FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:16:27.595346Z
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 4ab129e2-7a9d-4f01-8f63-6de9898d8ddc · inbound
Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.
Concordia: Self-Improving Synthetic Tables for Federated LLMs FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T22:23:47.942647Z
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.