FLaaS: Cross-App On-device Federated Learning in Mobile Environments
Kleomenis Katevas, Diego Perino, Nicolas Kourtellis

TL;DR
This paper introduces FLaME, a practical system enabling secure, cross-app federated learning on mobile devices, addressing technical challenges and demonstrating feasibility through extensive experiments with Android users.
Contribution
The paper presents FLaME, an end-to-end system for intra- and inter-app federated learning on mobile devices, filling a gap in practical, deployable FL solutions for real-world mobile environments.
Findings
FLaME is feasible for real-world mobile deployment.
Secure cross-app FL training is achievable on Android devices.
Experimental results show benefits and practicality of the system.
Abstract
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, efficiency). However, there is still a lack of a practical system to enable easy collaborative cross-silo FL training, in the context of mobile environments. In this work, we bridge this gap and propose FLaME, 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 with different types of IID and NonIID data distributions, in a secure and easy to deploy fashion. Our design solves…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Wireless Networks and Protocols · Internet Traffic Analysis and Secure E-voting
