ADAPTIVE COMMUNICATION-EFFICIENT FEDERATED LEARNING ON REAL-WORLD DATA

Abstract

Federated learning is proposed by Google to provide decentralized intelligent services while addressing data isolation and user privacy issues. The key idea is that clients can collaboratively train machine learning models without sharing data. As federated learning achieves collaborative learning in a privacy-preserving manner, it attracts increasingly more attention from both industry and academia. However, in real-world federated learning implementations over heterogeneous networks, learning performance would be significantly constrained by data heterogeneity and limited communications. In this thesis, we shall improve the generalization and communication efficiency performance of federated learning systems, so as to combat the above challenges. Specifically, we focus on client selection, model updates between the clients and server, as well as learning with real-world data, where data quality is an important measure of data heterogeneity.