Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning

TL;DR

This paper introduces LoDMeta, a decentralized meta-learning algorithm that reduces communication costs and enhances privacy by using local parameters and random perturbations, achieving comparable accuracy to centralized methods.

Contribution

The paper proposes LoDMeta, a novel decentralized meta-learning approach that improves privacy and communication efficiency in multi-task settings with limited data.

Findings

LoDMeta achieves similar accuracy to centralized meta-learning.

LoDMeta enhances privacy protection for individual clients.

LoDMeta reduces communication overhead in decentralized settings.

Abstract

Distributed learning, which does not require gathering training data in a central location, has become increasingly important in the big-data era. In particular, random-walk-based decentralized algorithms are flexible in that they do not need a central server trusted by all clients and do not require all clients to be active in all iterations. However, existing distributed learning algorithms assume that all learning clients share the same task. In this paper, we consider the more difficult meta-learning setting, in which different clients perform different (but related) tasks with limited training data. To reduce communication cost and allow better privacy protection, we propose LoDMeta (Local Decentralized Meta-learning) with the use of local auxiliary optimization parameters and random perturbations on the model parameter. Theoretical results are provided on both convergence and privacy analysis. Empirical results on a number of few-shot learning data sets demonstrate that LoDMeta has similar meta-learning accuracy as centralized meta-learning algorithms, but does not require gathering data from each client and is able to better protect data privacy for each client.