Three Approaches for Personalization with Applications to Federated Learning

TL;DR

This paper systematically studies three personalization methods—user clustering, data interpolation, and model interpolation—in federated learning, providing theoretical guarantees and empirical performance evaluations for each approach.

Contribution

It introduces and analyzes three novel personalization approaches with learning guarantees and efficient algorithms applicable to any hypothesis class.

Findings

All three methods achieve theoretical learning guarantees.

Algorithms are model-agnostic and versatile.

Empirical results demonstrate effectiveness across scenarios.

Abstract

The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is possible to learn a personalized model per user. In this work, we present a systematic learning-theoretic study of personalization. We propose and analyze three approaches: user clustering, data interpolation, and model interpolation. For all three approaches, we provide learning-theoretic guarantees and efficient algorithms for which we also demonstrate the performance empirically. All of our algorithms are model-agnostic and work for any hypothesis class.