Exploiting Shared Representations for Personalized Federated Learning

TL;DR

This paper introduces a federated learning framework that learns a shared data representation across clients with personalized local heads, improving efficiency and performance in heterogeneous data environments.

Contribution

The paper proposes a novel federated learning algorithm that learns a shared low-dimensional representation with personalized local heads, backed by theoretical convergence guarantees.

Findings

Achieves linear convergence to the true representation.

Reduces problem dimension for each client.

Outperforms existing personalized federated learning methods.

Abstract

Deep neural networks have shown the ability to extract universal feature representations from data such as images and text that have been useful for a variety of learning tasks. However, the fruits of representation learning have yet to be fully-realized in federated settings. Although data in federated settings is often non-i.i.d. across clients, the success of centralized deep learning suggests that data often shares a global feature representation, while the statistical heterogeneity across clients or tasks is concentrated in the labels. Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. We prove that this method obtains linear convergence to the ground-truth representation with near-optimal sample complexity in a linear setting, demonstrating that it can efficiently reduce the problem dimension for each client. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions, for example in meta-learning and multi-task learning. Further, extensive experimental results show the empirical improvement of our method over alternative personalized federated learning approaches in federated environments with heterogeneous data.