Abstract
Academia and industry have developed several platforms to support the popular privacy-preserving distributed learning method -- Federated Learning (FL). However, these platforms are complex to use and require a deep understanding of FL, which imposes high barriers to entry for beginners, limits the productivity of data scientists, and compromises deployment efficiency. In this paper, 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 for customization by unifying simple API design, modular design, and granular training flow abstraction. With only a few lines of code, EasyFL empowers them with many out-of-the-box functionalities to accelerate experimentation and deployment. These practical functionalities are heterogeneity simulation, distributed training optimization, comprehensive tracking, and seamless deployment. They are proposed based on challenges identified in the proposed FL life cycle. Our implementations show that EasyFL requires only three lines of code to build a vanilla FL application, at least 10x lesser than other platforms. Besides, our evaluations demonstrate that EasyFL expedites training by 1.5x. It also improves the efficiency of experiments and deployment. We believe that EasyFL will increase the productivity of data scientists and democratize FL to wider audiences.
Abstract (translated)
URL
https://arxiv.org/abs/2105.07603
