Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2402.11505.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:57:24.925571Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T13:16:58.669038Z
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 6f57010f-4df7-434a-bbc3-2893d74b5313 · inbound
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA cites this paper.
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:57:24.925571Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae1ff209-c84f-459b-b12b-8d7d97bcbfec · inbound
FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model cites this paper.
FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T01:44:30.393366Z
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 37add713-bd64-480a-ac98-ad05fa1a5df1 · inbound
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design cites this paper.
FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:49:56.588435Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd136f6e-2fc7-418f-8590-f6fd666fe6bd · inbound
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data cites this paper.
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:38:49.539833Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d4bbea2e-6bd4-497b-8ca5-f0bd630085e9 · 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 Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T19:08:54.369245Z
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 18271e7b-d035-4402-8168-a5e94dde559d · inbound
FedSDR: Federated Self-Distillation with Rectification cites this paper.
FedSDR: Federated Self-Distillation with Rectification Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Reference 15
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T12:18:16.229864Z
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 e730c1e1-d646-45e4-b77d-54135d1902c0 · inbound
Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models cites this paper.
Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Reference 32
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T13:16:58.670594Z
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.