TL;DR
EasyFL is a low-code federated learning platform that simplifies experimentation and deployment, reducing coding effort and training overhead, thereby making FL more accessible and efficient for researchers and practitioners.
Contribution
This paper introduces EasyFL, the first low-code FL platform that combines simplicity, flexibility, and extensibility for diverse user expertise levels.
Findings
Requires only three lines of code to build FL applications
Expedites distributed training by 1.5x
Reduces deployment overhead and enhances efficiency
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 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…
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