arXiv Machine Learning June 3, 2026 By Hanwei Tan, Wentai Wu, Ligang He, Yijun Quan

Lethe: Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning

Read the original on arXiv Machine Learning →

arXiv:2601. 22601v2 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase designated client-level, class-level, or sample-level knowledge from a global model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 4

\textsc{Lethe}: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning

arXiv:2601. 22601v3 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase knowledge from a global model.

By Wentai Wu, Hanwei Tan, Yijun Quan, Haixia Peng, Ligang He, Bin Yang, C. L. Philip Chen

nlp benchmarks

arXiv Machine Learning

Jul 17

Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap

arXiv:2601. 19788v2 Announce Type: replace Abstract: Federated Continual Learning (FCL) leverages inter-client collaboration to better balance new knowledge acquisition and old knowledge retention on non-stationary data.

By Sixing Tan, Xianmin Liu

arXiv Machine Learning

2d ago

ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning

arXiv:2609.38833v1 Announce Type: new Abstract: Federated continual learning must integrate new tasks over time without losing earlier-task knowledge. Most existing methods attach an anti-forgetting...

By Sungmin Kang, Zhengzhong Tu, Sunwoo Lee

arXiv AI

Jun 3

PURGE: Projected Unlearning via Retain-Guided Erasure

arXiv:2606. 03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems.

By Vedant Jawandhia, Daksh Ahuja, Ghufran Alam Siddiqui, Prashant Trivedi, Yash Sinha, Pratik Narang

safety

arXiv Machine Learning

Aug 3

When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks

arXiv:2607. 28829v1 Announce Type: cross Abstract: Self-improving federated agent networks keep training after deployment by collecting new trajectories with the current policy and feeding them back into later rounds.

By Zihao Ding, Jun Huang, Liang Dong

agents multimodal

arXiv Machine Learning

Jun 16

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning

arXiv:2606. 16304v1 Announce Type: new Abstract: Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR).

By Zhuodong Liu, Xiangyu Li, Zhihao Zhang

benchmarks safety

One email a morning, machine-written

Related stories

\textsc{Lethe}: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning

Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap

ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning

PURGE: Projected Unlearning via Retain-Guided Erasure

When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning