Academic Background and Problem Statement With the increasing prominence of data privacy issues, Federated Learning (FL) has emerged as a decentralized machine learning paradigm, allowing multiple clients to collaboratively train a global model without sharing data, thereby protecting data privacy. However, existing FL methods typically assume that...

A Unified Momentum-based Paradigm for Decentralized SGD for Non-Convex Models and Heterogeneous Data Research Background In recent years, with the rise of the Internet of Things and edge computing, distributed machine learning has developed rapidly, especially the decentralized training paradigm. However, in practical scenarios, non-convex objectiv...