arXiv Machine Learning October 5, 2026 By Mohammadjavad Ebrahimi, Daniel Burbano, Farzad Yousefian

A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning

Read the original on arXiv Machine Learning →

arXiv:2603. 19617v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient framework for solving distributed optimization problems.

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.

agents

arXiv Machine Learning

Sep 21

FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

arXiv:2609.21057v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic c...

By Herlock Rahimi, Dionysis Kalogerias

benchmarks safety

arXiv Machine Learning

4d ago

Best of Both Worlds in Federated LSA: Speedup When Possible, Personalization Always

The paper introduces PF‑LSA, a minimalist algorithm for personalized federated linear stochastic approximation that blends each agent’s local update with a global average. It achieves both fast convergence to each agent’s personalized solution and a linear speedup in the number of agents when their learning problems are similar, without requiring prior knowledge of heterogeneity. The analysis decomposes error into consensus and disagreement components, showing rapid decay of consensus error and negligible disagreement in low‑heterogeneity regimes.

By Safwan Labbi, Paul Mangold, Eric Moulines

agents

arXiv AI

1d ago

Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning

arXiv:2505. 11304v4 Announce Type: replace-cross Abstract: Federated learning (FL) commonly involves clients with diverse communication and computational capabilities.

By Shudi Weng, Chao Ren, Ming Xiao, Mikael Skoglund

benchmarks

arXiv Machine Learning

Aug 10

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.

By Grigory Malinovsky

fine-tuning

arXiv Machine Learning

Aug 11

Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum

arXiv:2504. 12742v2 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server.

By Yuan Zhou, Xinli Shi, Xuelong Li, Jiachen Zhong, Guanghui Wen, Jinde Cao

benchmarks

arXiv Machine Learning

Oct 5

Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems

arXiv:2307. 07113v2 Announce Type: replace-cross Abstract: In this paper, we consider the decentralized, stochastic nonconvex strongly-concave (NCSC) minimax problem with nonsmooth regularization terms on both primal and dual variables, wherein a network of $m$ computing agents collaborate via peer-to-peer communications.

By Gabriel Mancino-Ball, Muhammad Khan, Yangyang Xu

agents

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