Smart Multi-tenant Federated Learning

Weiming Zhuang, Yonggang Wen, Shuai Zhang

Keywords: federated learning, multi-tenant federated learning

TL;DR: We propose a smart multi-tenant federated learning system, MuFL, to efficiently coordinate and execute simultaneous training activities under resource constraints by considering both synergies and differences among training activities.

Abstract: Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous training activities could overload resource-constrained devices. In this work, we propose a smart multi-tenant FL system, MuFL, to effectively coordinate and execute simultaneous training activities. We first formalize the problem of multi-tenant FL, define multi-tenant FL scenarios, and introduce a vanilla multi-tenant FL system that trains activities sequentially to form baselines. Then, we propose two approaches to optimize multi-tenant FL: 1) activity consolidation merges training activities into one activity with a multi-task architecture; 2) after training it for rounds, activity splitting divides it into groups by employing affinities among activities such that activities within a group have better synergy. Extensive experiments demonstrate that MuFL outperforms other methods while consuming 40% less energy. We hope this work will inspire the community to further study and optimize multi-tenant FL.

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