Three Approaches for Personalization with Applications to Federated Learning

Yishay Mansour; Mehryar Mohri; Jae Ro; Ananda Theertha Suresh

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arXiv · 2002.10619

Three Approaches for Personalization with Applications to Federated Learning

Yishay Mansour·Mehryar Mohri·Jae Ro·Ananda Theertha Suresh

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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.

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Yishay Mansour, Mehryar Mohri, Jae Ro, Ananda Theertha Suresh. 2020-02-25. Three Approaches for Personalization with Applications to Federated Learning. https://arxiv.org/abs/2002.10619

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