Adaptive Dual-Mode Distillation for Robust and Communication-Efficient Federated Learning Under Statistical and Model Heterogeneity
The growing volume of data from smart devices offers significant potential for machine learning, yet privacy concerns hinder centralized use. Federated Learning (FL) has emerged as a promising decentralized learning (DL) approach enabling the use of distributed data without compromising privacy. However, practical depl...