arXiv Machine Learning June 10, 2026 By Yiyuan She, Zhaojun Hu, Yifan Sun

Range Penalization: Theoretical Insights with Applications in Federated Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 10916v1 Announce Type: cross Abstract: This paper introduces range regularization for federated learning with linear systematic components to enhance statistical accuracy and induce cross-client regularity conducive to quantization, coding, and resource efficiency.

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

efficiency

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 AI

Jul 2

Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning

arXiv:2607. 00275v1 Announce Type: cross Abstract: Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data.

By Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas Lundell

efficiency benchmarks

arXiv Machine Learning

Jul 7

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.

By Liyang Yuan, Yibo Yang, Dandan Guo

benchmarks

arXiv Machine Learning

Jun 10

FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection

arXiv:2604. 24012v3 Announce Type: replace Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments.

By Yutong He, Zhengyang Huang, Jiahe Geng, Kun Yuan

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

rag benchmarks

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.

By Shiyuan Zuo, Jiashuo Li, Rongfei Fan, Han Hu, Jie Xu

rag benchmarks

Related stories

Federated Learning for Feature Generalization with Convex Constraints

Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee