A Field Guide to Federated Optimization

TL;DR

This paper offers practical guidelines and recommendations for designing, evaluating, and analyzing federated optimization algorithms, emphasizing communication efficiency, data heterogeneity, and privacy considerations in real-world applications.

Contribution

It provides concrete examples and practical insights to help researchers develop effective federated learning algorithms tailored for diverse real-world scenarios.

Findings

Guidelines for formulating federated optimization problems

Recommendations for simulation and evaluation methods

Insights into balancing privacy, efficiency, and heterogeneity

Abstract

Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.