Paper Citation Record · LEDGER

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning

As of 4 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2506.23210.

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

pith.paper-citation-record.v1
2506.23210 v5

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T08:01:36.335624Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

23 of 23 outbound references displayed

verified exact4
verified fuzzy19
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12837c4d-3fce-4f49-92fe-9121d3b1eca0 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Communication-efficient learning of deep networks from decentralized data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.632473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b6c52b71-3a6d-40dc-936b-f0f8a95cdfa9 · outbound

This paper cites Catastrophic interference in connec- tionist networks: The sequential learning problem.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Catastrophic interference in connec- tionist networks: The sequential learning problem

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.622579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d67279ba-d16a-491a-a614-8c88947ff672 · outbound

This paper cites Catastrophic forgetting in connectionist networks.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Catastrophic forgetting in connectionist networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.627797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8e1d319b-94b4-4b19-b424-aff30a4e6bcc · outbound

This paper cites An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-19T08:02:10.192380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 57f72ad6-cf1f-4658-9724-4c10105c2e77 · outbound

This paper cites Reducing the hausdorff distance in medical image segmentation with convolutional neural networks.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Reducing the hausdorff distance in medical image segmentation with convolutional neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.561564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 76e8b458-9260-43f5-9d17-422e3332a377 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-19T08:02:10.201300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 64987376-1194-4d55-804e-e3ca5df6154b · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Unetr: Transformers for 3d medical image segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.557535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 44b90f03-a8ca-489e-9d7e-d0da7a384744 · outbound

This paper cites Medical lesion segmentation by combining multimodal images with modality weighted unet.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Medical lesion segmentation by combining multimodal images with modality weighted unet

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.553349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 24cf41cc-0809-43f3-bc33-b89ec45db355 · outbound

This paper cites Revisiting Distributed Synchronous SGD.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Revisiting Distributed Synchronous SGD

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-19T08:02:10.208527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b7427f19-8957-45c0-9624-2cf87b6781d4 · outbound

This paper cites Federated optimization in heterogeneous networks.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Federated optimization in heterogeneous networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.548675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f2e43a55-8291-45b2-b6f3-8628f48c0cf0 · outbound

This paper cites Adaptive federated optimization.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Adaptive federated optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.617736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6670942d-fbc6-4730-a827-7952c732d311 · outbound

This paper cites Feddyn: A dynamic and efficient federated distillation approach on recommender system.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Feddyn: A dynamic and efficient federated distillation approach on recommender system

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.613796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1a5da213-500d-4159-97fa-2cc9a50492b0 · outbound

This paper cites Bayesian parameter-efficient fine-tuning for overcoming catastrophic forgetting.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Bayesian parameter-efficient fine-tuning for overcoming catastrophic forgetting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.609547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93f7eded-7e09-4abb-bacd-053783782800 · outbound

This paper cites A practical bayesian framework for backpropagation networks.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning A practical bayesian framework for backpropagation networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.604114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 44611f66-4c0f-43d8-bcd8-bfd3b5e2637c · outbound

This paper cites Explicit inductive bias for transfer learning with convolutional networks.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Explicit inductive bias for transfer learning with convolutional networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.598589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 71e0e7b7-ba20-4189-b0c9-846421fd6e93 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Overcoming catastrophic forgetting in neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.593760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4b4e0724-ebbc-4edd-84cf-670e173ad334 · outbound

This paper cites Optimizing neural networks with kronecker- factored approximate curvature.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Optimizing neural networks with kronecker- factored approximate curvature

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.589393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b103d22-4514-4d91-a22a-82328528c7c3 · outbound

This paper cites Fast approximate natural gradient descent in a kronecker factored eigenbasis.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Fast approximate natural gradient descent in a kronecker factored eigenbasis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.566651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 30215ff1-949d-4889-8619-232c834bce83 · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Flower: A Friendly Federated Learning Research Framework

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:07:23.126952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 12a4d6a8-6ed3-40df-8ea8-830d86924e9c · outbound

This paper cites Mnist handwritten digit database.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Mnist handwritten digit database

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.583988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8b592668-8d8b-417d-89a2-8092b7219265 · outbound

This paper cites Searching for mobilenetv3.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Searching for mobilenetv3

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.579272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 83ddf7bd-7b49-4aea-afed-7910691598d9 · outbound

This paper cites Federated Tumor Segmentation (FeTS) 2022 Dataset.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Federated Tumor Segmentation (FeTS) 2022 Dataset

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.575443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 79b84471-a26c-4fd8-85c9-daa8f15a640f · outbound

This paper cites Ridge-based vessel segmentation in color images of the retina.

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Ridge-based vessel segmentation in color images of the retina

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T08:02:11.571494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.