Private Multi-Task Learning: Formulation and Applications to Federated Learning

Abstract

Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relevant for privacy-sensitive applications in areas such as healthcare, finance, and IoT computing, where sensitive data from multiple, varied sources are shared for the purpose of learning. In this work, we formalize notions of client-level privacy for MTL via joint differential privacy (JDP), a relaxation of differential privacy for mechanism design and distributed optimization. We then propose an algorithm for mean-regularized MTL, an objective commonly used for applications in personalized federated learning, subject to JDP. We analyze our objective and solver, providing certifiable guarantees on both privacy and utility. Empirically, we find that our method provides improved privacy/utility trade-offs relative to global baselines across common federated learning benchmarks.

Accepted to TMLR. Transactions on Machine Learning Research (2022)

References in corpus (6)

• Adaptive Personalized Federated Learning
• Three Approaches for Personalization with Applications to Federated Learning
• Federated Learning of a Mixture of Global and Local Models
• Salvaging Federated Learning by Local Adaptation
• Lower Bounds and Optimal Algorithms for Personalized Federated Learning
• A Theoretical Perspective on Differentially Private Federated Multi-task Learning