arXiv Machine Learning
FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Oct 5
A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning
arXiv:2603. 19617v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient framework for solving distributed optimization problems.
arXiv Machine Learning
Jun 2
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.
arXiv Machine Learning
Sep 7
Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning
The paper introduces FedSWE, a federated learning algorithm designed to handle non‑stationary and heterogeneous client availability without requiring prior real‑time knowledge of which devices are online. FedSWE compensates for missed computations, stabilizes global updates, and mixes local updates through implicit gossiping, all while adding only modest memory and computational overhead. The authors prove that FedSWE converges to a stationary point for non‑convex objectives and achieves linear speedup in certain scenarios, and they validate these claims with experiments on real‑world datasets featuring diverse client unavailability patterns.
arXiv AI
Oct 12
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.
arXiv Machine Learning
Sep 30
Byzantine-Robust Federated Representation Learning
arXiv:2609.36660v1 Announce Type: new Abstract: We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients witho...
arXiv Machine Learning
Jul 14
First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
arXiv:2603. 05774v2 Announce Type: replace Abstract: This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints.