2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2026
FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning
Van Truong Vo, Khoa Nguyen, Taehong Kim
Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). However, DFL suffers from conver…
cs.AI2026★ 2 cited
FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks
Majid Kundroo, Tinku Singh, Taehong Kim
Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy. However, efficient model convergence in FL remains challeng…