Hugging Face Trending Papers August 10, 2026

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

Read the original on Hugging Face Trending Papers →

Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). However, DFL suffers from convergence inefficiency under data heterogeneity due to the use of a uniform learning rate (LR) that ignores layer-specific optimization needs.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

Aug 11

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

arXiv:2608. 09208v1 Announce Type: cross Abstract: Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL).

By Van Truong Vo, Khoa Nguyen, Taehong Kim

arXiv Machine Learning

Jun 8

Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers

arXiv:2606. 06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers.

By Su Wang, Mung Chiang, H. Vincent Poor

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 AI

Aug 10

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.

By Majid Kundroo, Tinku Singh, Taehong Kim

safety

arXiv Machine Learning

Jun 26

Chisme: Heterogeneity-Aware Gossip Learning

arXiv:2505. 09854v3 Announce Type: replace Abstract: As end-user device capability increases and demand for intelligent services at the Internet's edge rises, distributed learning has emerged as a key enabling technology for the intelligent edge.

By Harikrishna Kuttivelil, Katia Obraczka

computer-vision benchmarks safety

arXiv Machine Learning

Jun 16

Communication-Efficient Neural Tangent Kernels for Heterogeneous Decentralized Federated Learning

arXiv:2512. 12737v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) enables collaborative model training without a central server, but converges slowly under statistical heterogeneity.

By Li Xia

data-engineering

Related stories

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers

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

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

Chisme: Heterogeneity-Aware Gossip Learning

Communication-Efficient Neural Tangent Kernels for Heterogeneous Decentralized Federated Learning