FLINT: A Platform for Federated Learning Integration

TL;DR

This paper introduces FLINT, a platform that facilitates the integration of federated learning into existing systems, enabling real-world evaluation, resource assessment, and decision-making for large-scale deployment.

Contribution

The paper presents a comprehensive device-cloud federated learning platform with tools for performance measurement, infrastructure assessment, and trade-off evaluation, addressing deployment challenges.

Findings

Empirical evaluation of FL in business-critical applications affecting hundreds of millions.

Tools for measuring real-world constraints and system resource requirements.

Decision workflow for evaluating trade-offs in cross-device federated learning.

Abstract

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to cross-device FL for millions or billions of devices presents many risks, including performance loss, developer inertia, poor user experience, and unexpected application failures. In addition, the corresponding infrastructure, development costs, and return on investment are difficult to estimate. In this paper, we present a device-cloud collaborative FL platform that integrates with an existing machine learning platform, providing tools to measure real-world constraints, assess infrastructure capabilities, evaluate model training performance, and estimate system resource requirements to responsibly bring FL into production. We also present a decision workflow that leverages the FL-integrated platform to comprehensively evaluate the trade-offs of cross-device FL and share our empirical evaluations of business-critical machine learning applications that impact hundreds of millions of users.