Federated Learning Platform
Last updated on Mar 9, 2022
My current research interests include federated learning, computer vison, self-supervised learning, and machine learning system.
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.
Weiming Zhuang, Yonggang Wen, Shuai Zhang
We propose the first low-code FL platform, EasyFL, to enable users with various levels of expertise to experiment and prototype FL applications with little coding. We achieve this goal while ensuring great flexibility and extensibility for customization by unifying simple API design, modular design, and granular training flow abstraction. Besides, EasyFL expedites distributed training by 1.5x.
Weiming Zhuang, Xin Gan, Yonggang Wen, Shuai Zhang