arXiv AI June 30, 2026 By Wenhao Yuan, Chenchen Lin, Jian Chen, Jinfeng Xu, Zewei Liu, Edith Cheuk Han Ngai

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning

arXiv:2606. 28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Jun 16

Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.

By Yijun Lu, Zihan Fang, Pengpeng Qiao, Zheng Lin, Jing Yang, Yuxin Zhang, Por Lip Yee, Zhe Chen, Jun Luo

llms fine-tuning safety

arXiv Machine Learning

1d ago

FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting

arXiv:2608. 15310v1 Announce Type: cross Abstract: Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for effective distributed modeling with data privacy preservation.

By Zhenyan Liu, Hua Zhang, Haoran Gao, Qi Li, Hongliang Zhu, Huiyu Zhou, Zongliang Shen, Yanxin Xu, Jiahui Wang

multimodal safety

arXiv AI

Jun 8

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

arXiv:2606. 07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge.

By Fatema Siddika, Md Anwar Hossen, Tanwi Mallick, Ali Jannesari

llms benchmarks

arXiv AI

1d ago

When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation

arXiv:2608. 15639v1 Announce Type: cross Abstract: \textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients.

By Wenhao Yuan, Chenchen Lin, Wenhao Hu, Jian Chen, Jinfeng Xu, Shujie Li, Edith Cheuk Han Ngai

benchmarks safety

arXiv Machine Learning

Jul 1

Expected Gain-based Escalation in Vertical Federated Learning

arXiv:2606. 31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to every sample can incur unnecessary communication and computational overhead.

By Mohamad Mestoukirdi, Vincent Corlay

rag agents benchmarks

arXiv Machine Learning

Jun 30

Personalized Additive Modeling for Multi-level Federated Learning

arXiv:2405. 16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization.

By Shutong Chen, Guodong Long, Tianyi Zhou, Jie Ma, Jing Jiang, Chengqi Zhang

Related stories

Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation

Expected Gain-based Escalation in Vertical Federated Learning

Personalized Additive Modeling for Multi-level Federated Learning