Paper Citation Record · LEDGER

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning

As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2605.27900.

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

pith.paper-citation-record.v1
2605.27900 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T13:36:39.308309Z

measured 20 of 20 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact10
verified fuzzy0
unresolved8
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7e5cbbb5-71db-423d-ae61-b21e4b810209 · outbound

This paper cites an unresolved cited work.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b622b3b-8aa0-4bf4-b5ad-b3f01523c4a9 · outbound

This paper cites Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:43:29.195074Z

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.

Observation f75ec219-ee53-4af9-b2af-90c446196151 · outbound

This paper cites Geodesic flow kernel for unsupervised domain adaptation.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Geodesic flow kernel for unsupervised domain adaptation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75f5964b-7861-4480-aa8d-5eab16160552 · outbound

This paper cites pfedprompt: Learning per- sonalized prompt for vision-language models in federated learning.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning pfedprompt: Learning per- sonalized prompt for vision-language models in federated learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cfc9c5e-e571-466b-8a9e-6273a72453cb · outbound

This paper cites Dcp: Dual-cue pruning for efficient large vision-language models.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Dcp: Dual-cue pruning for efficient large vision-language models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d43e7aa4-8543-48e2-8b69-1fd0d49a808b · outbound

This paper cites Federated Reinforcement Learning with Constraint Heterogeneity.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Federated Reinforcement Learning with Constraint Heterogeneity

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:43:29.188698Z

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.

Observation 06cf7856-0ea7-4696-b75a-f429f91643ae · outbound

This paper cites FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:43:29.190086Z

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.

Observation 97f5c781-df12-42cd-bcb2-ec21568eaea6 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Proximal Policy Optimization Algorithms

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-06-29T13:53:28.995029Z

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.

Observation 480f2634-9e0d-49f6-b011-7c161fccf187 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T13:53:29.000484Z

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.

Observation 21a2a530-53b6-4e8d-b9e2-1136e02345d2 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-06-29T13:43:29.193233Z

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.

Observation a5a17d79-1da3-4f17-8961-d5a5f015a67d · outbound

This paper cites Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:53:28.992487Z

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.

Observation 26f56d35-a3e7-4ad3-83de-48ac5d306ec1 · outbound

This paper cites an unresolved cited work.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 319e60af-b90a-4c72-9ecd-47088138b455 · outbound

This paper cites an unresolved cited work.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21a0817e-df76-4f1c-8a47-27a1a086e7fa · outbound

This paper cites Ablation Study.We conduct ablation studies on seven datasets (CIFAR100, Tiny-ImageNet, OxfordPet, Flower102, Caltech101, Caltech256, Food101).

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Ablation Study.We conduct ablation studies on seven datasets (CIFAR100, Tiny-ImageNet, OxfordPet, Flower102, Caltech101, Caltech256, Food101)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a4d1302-72c8-43ef-9105-a05185341be5 · outbound

This paper cites We further introduce label skew (IID, Dirichlet(0.1), Dirichlet(0.3), Dirichlet(0.5)) on top of the feature shift.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning We further introduce label skew (IID, Dirichlet(0.1), Dirichlet(0.3), Dirichlet(0.5)) on top of the feature shift

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:53:29.003427Z

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.

Observation beae2c05-f254-4617-92ba-230f5de9b81f · outbound

This paper cites As a result, the simple accuracy-based signal is sufficient for stage transition.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning As a result, the simple accuracy-based signal is sufficient for stage transition

Reference 16

Resolution: malformed identifier; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5d0b501-c22b-42c4-8a8d-1cd2334b8b5a · outbound

This paper cites For numerical stability, both the product and clipping operations are implemented in log space.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning For numerical stability, both the product and clipping operations are implemented in log space

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:43:29.196261Z

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.

Observation b3716a96-6359-4d15-a6d3-5568057f36dc · outbound

This paper cites Per-dataset results in Table.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning Per-dataset results in Table

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T13:36:39.308309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b1ccb1f-e59e-4ad1-b62b-1195949969b4 · outbound

This paper cites However, the accuracy on novel classes with mismatched backbones (i.e., ViT-L/14 and ViT-B/32) declines significantly, demonstrating generalization degradation.

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning However, the accuracy on novel classes with mismatched backbones (i.e., ViT-L/14 and ViT-B/32) declines significantly, demonstrating generalization degradation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:53:28.997778Z

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.

Observation 3fee48b6-98c1-4a5f-9bb6-0800dbefd740 · outbound

This paper cites one”) and the feature shift with label skew (Dirichlet(α=0.1)) setting (“Dir(0.1).

Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning one”) and the feature shift with label skew (Dirichlet(α=0.1)) setting (“Dir(0.1)

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:43:29.191547Z

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.

Pith citing papers

No inbound Pith citation observations are available.