Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity

07/01/2022
by   Samuel Horvath, et al.
0

Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Konečný et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centralized machine learning since, by construction, the training data points are decentralized and never transferred by the clients to a central server. Therefore, to a certain degree, FL mitigates the privacy risks associated with centralized data collection. Unfortunately, optimization for FL faces several specific issues that centralized optimization usually does not need to handle. In this thesis, we identify several of these challenges and propose new methods and algorithms to address them, with the ultimate goal of enabling practical FL solutions supported with mathematically rigorous guarantees.

READ FULL TEXT
research
12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...
research
08/08/2023

A Survey on Decentralized Federated Learning

In recent years, federated learning (FL) has become a very popular parad...
research
08/16/2021

Reducing the Communication Cost of Federated Learning through Multistage Optimization

A central question in federated learning (FL) is how to design optimizat...
research
10/17/2020

Secure Weighted Aggregation in Federated Learning

Federated learning (FL) schemes enable multiple clients to jointly solve...
research
02/20/2023

Federated Gradient Matching Pursuit

Traditional machine learning techniques require centralizing all trainin...
research
05/16/2019

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

Access to sufficient annotated data is a common challenge in training de...
research
01/24/2022

Towards Multi-Objective Statistically Fair Federated Learning

Federated Learning (FL) has emerged as a result of data ownership and pr...

Please sign up or login with your details

Forgot password? Click here to reset