FLaaS: Enabling Practical Federated Learning on Mobile Environments

Federated Learning (FL) has recently emerged as a popular solution to distributedly train a model on user devices improving user privacy and system scalability. Major Internet companies have deployed FL in their applications for specific use cases (e.g., keyboard prediction or acoustic keyword trigger), and the research community has devoted significant attention to improving different aspects of FL (e.g., accuracy, privacy). However, there is still a lack of a practical system to easily enable FL training in the context of mobile environments. In this work, we bridge this gap and propose FLaaS, an end-to-end system (i.e., client-side framework and libraries, and central server) to enable intra- and inter-app training on mobile devices, in a secure and easy to deploy fashion. Our design solves major technical challenges such as on-device training, secure and private single and joint-app model training while being offered in an "as a service" model. We implement FLaaS for Android devices and experimentally evaluate its performance in-lab and in-wild, on more than 140 users for over a month. Our results show the feasibility and benefits of the design in a realistic mobile context and provide several insights to the FL community on the practicality and usage of FL in the wild.