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 researchers, 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 and extensibility 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, comprehensive tracking, distributed training optimization, and seamless deployment. They are proposed based on challenges identified in the proposed FL life cycle. Compared with other platforms, EasyFL not only requires just three lines of code (at least 10x lesser) to build a vanilla FL application but also incurs lower training overhead. Besides, our evaluations demonstrate that EasyFL expedites distributed training by 1.5x. It also improves the efficiency of deployment. We believe that EasyFL will increase the productivity of researchers and democratize FL to wider audiences.
翻译:学术界和产业已经开发了几个平台,以支持公众隐私保护分布式学习方法 -- -- 联邦学习联合会(FL),但这些平台使用起来复杂,需要深入理解FL,这给初学者的进入设置了很高的壁垒,限制了研究人员的生产力,降低了部署效率。在本文件中,我们提出了第一个低编码FL平台,即FAFL, 使具有不同水平专门知识的用户能够试验和原型FL应用程序,而没有多少编码。我们通过统一简单的API设计、模块设计和颗粒式培训流程的抽象化,在确保极大灵活性和可扩大定制性的同时,我们实现了这个目标。只有几行代码,LFA才能赋予他们能力,使其拥有许多非箱功能,以加速试验和部署。这些实际功能是异质模拟、全面跟踪、分散培训优化和无缝部署。这些功能是根据拟议FL生命周期中确定的挑战提出的。与其他平台相比,LAFA不仅需要3行(至少10行)来构建一个香草 FL应用程序,而且需要降低培训效率。此外,LAFL还能更快地提高培训效率。