arXiv Machine Learning August 13, 2026 By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.

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

arXiv Machine Learning

Jun 15

FedSPC: Shared Parameter Correction for Personalized Federated Learning

arXiv:2606. 13748v1 Announce Type: new Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation.

By Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu, Toru Hirano

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

Jun 15

Federated Learning for Feature Generalization with Convex Constraints

arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.

By Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim

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

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 AI

Jul 28

AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

arXiv:2512. 24625v3 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management.

By Zijian Zhao, Yitong Shang, Sen Li

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