Paper Citation Record · LEDGER

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting

As of 13 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 3 inbound Pith citation observations for arXiv:2501.15356.

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

pith.paper-citation-record.v1
2501.15356 v3

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:26:58.213488Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T13:54:38.393967Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:54:52.951106Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy5
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 408e6af8-4c63-4170-9862-a6fe2fbd15d4 · outbound

This paper cites Podnet: Pooled outputs distillation for small-tasks incremental learning.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Podnet: Pooled outputs distillation for small-tasks incremental learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:26:58.411723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ce614c16-6920-475e-b5d4-2b2b03cf35c1 · outbound

This paper cites Auto-Encoding Variational Bayes.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Auto-Encoding Variational Bayes

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.160703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0c84919-6199-42fb-9c97-6f5d05343440 · outbound

This paper cites A Novel Attribute Reconstruction Attack in Federated Learning.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting A Novel Attribute Reconstruction Attack in Federated Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.172889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 707c56e3-b4d0-4e63-81ed-1488a849f533 · outbound

This paper cites Class-incremental learning: survey and performance evaluation on image classification.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Class-incremental learning: survey and performance evaluation on image classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.177057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34272ef0-5070-4ffe-b132-5f1d9c854dda · outbound

This paper cites Federated Adversarial Domain Adaptation.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Federated Adversarial Domain Adaptation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.181535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bfb1a22-6c5b-485f-a868-df7edb24d1de · outbound

This paper cites Asynchronous federated continual learning.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Asynchronous federated continual learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:26:58.386631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a3a3e999-81de-4e0b-ae44-de10a09df137 · outbound

This paper cites Overcoming Forgetting in Federated Learning on Non-IID Data.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Overcoming Forgetting in Federated Learning on Non-IID Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.193625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 308af70e-fd95-4058-aa36-f066f27a2820 · outbound

This paper cites Incremental Learning of Structured Memory via Closed-Loop Transcription.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Incremental Learning of Structured Memory via Closed-Loop Transcription

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.197620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b803aba-39fd-425b-a138-c150450a570a · outbound

This paper cites Federated Learning with Matched Averaging.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Federated Learning with Matched Averaging

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.202046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cc61204-e322-4d55-82b4-35bb6989f207 · outbound

This paper cites Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.206084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2ac55dc-5412-4946-8431-9b5e0d67784d · outbound

This paper cites Fine-tuning global model via data-free knowledge distillation for non-iid federated learning.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Fine-tuning global model via data-free knowledge distillation for non-iid federated learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:26:58.375603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a7dfc4fd-7f5f-4c10-8d9b-fc641ef46699 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Federated Learning: Strategies for Improving Communication Efficiency

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.164451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdc47177-e3d0-40bb-9a09-6774ff64d30c · outbound

This paper cites icarl: Incremental classifier and representation learning.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting icarl: Incremental classifier and representation learning

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:26:58.399554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c05912a0-6f3d-4dde-9d24-62783789e7ce · outbound

This paper cites FedBN: Federated Learning on Non-IID Features via Local Batch Normalization.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.168874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b664953-d285-4a21-9aa7-a214b6ba254a · outbound

This paper cites Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:58.148077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e82513fe-0694-42c7-a121-31bae19dc2bf · outbound

This paper cites Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation

Reference 2022

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T14:26:58.250090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6eb4e71e-94fd-497d-b46c-71e5105db0e2 · outbound

This paper cites A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks.

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:26:58.423023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 7f62b513-7482-422f-a0b9-3266459451ec · inbound

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning cites this paper.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.954222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e735d90f-aff9-471c-b5d5-65b17c910857 · inbound

Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins cites this paper.

Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:51:25.573510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b7dd9b24-40c5-4ec2-9fd0-7cbb37b97e7a · inbound

From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity cites this paper.

From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:35:57.466263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.