arXiv Machine Learning
Adaptive Determinantal Client Scheduling in Federated Learning
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
Jun 24
FAIRVAR: Fair Federated Learning via Variance Regularization
arXiv:2508. 12042v3 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data.
arXiv Machine Learning
Jul 21
Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
arXiv Machine Learning
Sep 15
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
arXiv:2311.03154v3 Announce Type: replace Abstract: There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients tra...
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 Machine Learning
Aug 31
Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning
The paper addresses the mismatch between learner and client data distributions in federated learning, noting that traditional client selection methods often ignore this misalignment. It introduces a dynamic, influence-aware client selection framework that uses a small proxy dataset to estimate each client's utility for the learner’s objective, prioritizing informative sources while mitigating noise and heterogeneity. Experiments on CIFAR-10 with heterogeneous partitions show the proposed method outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.
arXiv AI
Jun 30
Federated Learning with Energy-Based Structured Probabilistic Inference
arXiv:2606. 30161v1 Announce Type: cross Abstract: Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model.