FLaaS: Federated Learning as a Service

TL;DR

This paper introduces FLaaS, a system that enables collaborative federated learning across third-party applications, addressing privacy, permission, and usability challenges, and demonstrates its feasibility on mobile devices for image detection tasks.

Contribution

The paper presents FLaaS, a novel framework for cross-application federated learning, including its implementation and evaluation on mobile devices for practical image detection applications.

Findings

FLaaS enables collaborative model building across applications.

On-device training costs are manageable in CPU, memory, and power.

FLaaS can train models across 100 devices in a few hours.

Abstract

Federated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on user devices, avoiding user data to be transferred to centralized servers, and can be enhanced with differential privacy mechanisms. Although FL has been recently deployed in real systems, the possibility of collaborative modeling across different 3rd-party applications has not yet been explored. In this paper, we tackle this problem and present Federated Learning as a Service (FLaaS), a system enabling different scenarios of 3rd-party application collaborative model building and addressing the consequent challenges of permission and privacy management, usability, and hierarchical model training. FLaaS can be deployed in different operational environments. As a proof of concept, we implement it on a mobile phone setting and discuss practical implications of results on simulated and real devices with respect to on-device training CPU cost, memory footprint and power consumed per FL model round. Therefore, we demonstrate FLaaS's feasibility in building unique or joint FL models across applications for image object detection in a few hours, across 100 devices.