3 papers
cs.SD2025
FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
Jun Bai, Rajib Rana, Di Wu +5
Federated Learning (FL) offers a privacy-preserving framework for training audio classification (AC) models across decentralized clients without sharing raw data. However, Federate…
cs.DC2025
A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning
Jun Bai, Yiliao Song, Di Wu +6
One-Shot Federated Learning (OSFL) restricts communication between the server and clients to a single round, significantly reducing communication costs and minimizing privacy leaka…
cs.LG2024
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning
Jiayu Liu, Yong Wang, Nianbin Wang +2
Federated Learning (FL) is an innovative distributed machine learning paradigm that enables neural network training across devices without centralizing data. While this addresses i…