FLaaS: Enabling Practical Federated Learning on Mobile Environments

• Contributors: Kleomenis Katevas, Diego Perino, Nicolas Kourtellis
• Year: 2022
• Venue:
• Abstract:

  Federated Learning (FL) [2] has emerged as a popular solution of Confidential Computing [3] to distributedly train a model on user devices, improving privacy and system scalability. Such privacy-preserving models can be used in wide range of applications, and especially in Telco networks [4]. However, there are no practical systems to easily enable FL training on mobile apps, and especially in an as-a-service fashion. In this demo, we implement and test FLaaS, our recently proposed end-to-end FL service [1]. FLaaS includes a client-side framework with app library and service, and a back-end server, to enable secure and easy to deploy intra- and inter-app FL model training on mobile environments.

• Repository link: https://dl.acm.org/doi/10.1145/3498361.3539693
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