Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.18848.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T16:22:25.538325Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T14:08:21.886578Z
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 e817d1b6-07e8-4165-ac66-ac64e1b72cc0 · inbound
Aggregating Low Rank Adapters in Federated Fine-tuning cites this paper.
Aggregating Low Rank Adapters in Federated Fine-tuning FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T05:25:26.404457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2f3d38ee-a558-4ccb-b7e9-40d4391afc26 · inbound
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge cites this paper.
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:22:25.538325Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 71112787-7fee-4d38-8ca1-39da6bb1b909 · inbound
A Survey: Towards Privacy and Security in Mobile Large Language Models cites this paper.
A Survey: Towards Privacy and Security in Mobile Large Language Models FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:39:19.242679Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 904c2e76-08e9-4381-a661-11c740aabda8 · inbound
FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation cites this paper.
FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T08:13:15.199516Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation be3e9e20-27a6-4304-9eb0-ca6748a0942e · inbound
The Hidden Power of Scaling Factor in LoRA Optimization cites this paper.
The Hidden Power of Scaling Factor in LoRA Optimization FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Reference 16
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T14:08:21.887930Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.