arXiv Machine Learning

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

arXiv AI

Jun 3

FlashbackCL: Mitigating Temporal Forgetting in Federated Learning

arXiv:2606. 03939v1 Announce Type: cross Abstract: Federated Learning (FL) of foundation and edge models increasingly targets deployments where client data distributions drift over time, yet existing forgetting-mitigation methods assume each client's distribution is stationary.

By Mubarak A. Ojewale, Adriana E. Chis, Jorge M. Cortes-Mendoza, Bernardo Pulido-Gaytan, Horacio Gonzalez-Velez

efficiency benchmarks

Hugging Face Trending Papers

Jun 2

FlashbackCL: Mitigating Temporal Forgetting in Federated Learning

Federated Learning (FL) of foundation and edge models increasingly targets deployments where client data distributions drift over time, yet existing forgetting-mitigation methods assume each client's distribution is stationary. Flashback, the strongest recent FL method against cross-client (spatial) forgetting, uses monotonically accumulating per-class label counts as a knowledge proxy; this proxy becomes miscalibrated under temporal distribution shift and anchors the global model to an outdated class balance.

efficiency 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

Jul 20

Rethinking Transfer in Continual Learning: A Replay-Based Realisation

arXiv:2607. 15587v1 Announce Type: new Abstract: Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch.

By Yang Meng, Zhenya Liu, Zhuokai Zhao, Yuxin Chen

llms efficiency

arXiv Machine Learning

Sep 15

Realistic Continual Learning Approach using Pre-trained Models

arXiv:2404.07729v2 Announce Type: replace Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forg...

By Nadia Nasri, Carlos Guti\'errez-\'Alvarez, Sergio Lafuente-Arroyo, Saturnino Maldonado-Basc\'on, Roberto J. L\'opez-Sastre

benchmarks

arXiv Machine Learning

Jun 9

Theoretical Foundations of Continual Learning via Drift-Plus-Penalty

arXiv:2606. 08452v1 Announce Type: new Abstract: In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch.

By Nazreen Shah, Govinda Arya, Bharath B. N., Ranjitha Prasad

benchmarks

arXiv Machine Learning

Jul 10

When Does Continual Learning Require Learning

arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?

By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 2

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning

arXiv:2606. 01379v1 Announce Type: new Abstract: While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limits later tasks from improving earlier ones, leaving backward knowledge transfer underexplored.

By Anushka Tiwari, Kaiyi Ji

llms benchmarks

arXiv Machine Learning

Jul 30

The Art of Not Forgetting A Local Learning Architecture for Continual Learning

arXiv:2607. 26523v1 Announce Type: new Abstract: We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?

By Ashmith Atmuri, Yashaswini Rao Bhogarajula

llms benchmarks

arXiv Machine Learning

Sep 16

CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework

arXiv:2609.17026v1 Announce Type: new Abstract: Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a da...

By Yunxiang Fu, Meng Lou, Zicheng Liao, Yizhou Yu

fine-tuning efficiency benchmarks

arXiv Machine Learning

Aug 13

Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning

arXiv:2608. 11690v1 Announce Type: new Abstract: Continual learning must absorb new tasks without erasing old ones, and replay---mixing a small buffer of past examples into current training---is among the most effective remedies for catastrophic forgetting.

By Tieliang Gong, Zhongbo Zhang, Wen Wen, Yong-Jin Liu

benchmarks safety

arXiv Machine Learning

2d ago

Federated Class-Incremental Learning with Hierarchical Generative Prototypes

arXiv:2406.02447v5 Announce Type: replace Abstract: Federated Learning (FL) aims at unburdening the training of deep models by distributing computation across multiple devices (clients) while safegua...

By Riccardo Salami, Pietro Buzzega, Matteo Mosconi, Mattia Verasani, Simone Calderara

fine-tuning safety

Related stories