arXiv Machine Learning July 14, 2026 By Ben Rachmut, Luise Ge, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication

Read the original on arXiv Machine Learning →

arXiv:2605. 11165v3 Announce Type: replace Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.

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

arXiv Machine Learning

Jun 2

Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering

arXiv:2303. 04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients.

By Xu Zhang, Wenpeng Li, Yunfeng Shao, Yonglin Liu, Kaiwen Zhou, Yinchuan Li

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arXiv AI

Jun 16

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

arXiv:2506. 22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL).

By Randeep Bhatia, Nikos Papadis, Murali Kodialam, TV Lakshman, Sayak Chakrabarty

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arXiv Machine Learning

6d ago

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

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

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arXiv Machine Learning

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Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data

arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.

By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada

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.

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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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