FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

TL;DR

FLeet introduces an online federated learning system that enhances model quality and efficiency on mobile devices by predicting performance impacts and adapting learning algorithms to ensure privacy and rapid convergence.

Contribution

The paper presents FLeet, the first online FL system with a novel profiler and adaptive algorithm, enabling real-time model updates with minimal energy consumption on mobile devices.

Findings

FLeet achieves a 2.3x improvement in model quality over standard FL.

FLeet consumes only 0.036% of battery per day.

I-Prof improves impact prediction accuracy by up to 3.6x in computation and 19x in energy.

Abstract

Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or performance impact on mobile devices, and are therefore not suitable for applications that require frequent (online) model updates, such as news recommenders. This paper presents FLeet, the first Online FL system, acting as a middleware between the Android OS and the machine learning application. FLeet combines the privacy of Standard FL with the precision of online learning thanks to two core components: (i) I-Prof, a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices, and (ii) AdaSGD, a new adaptive learning algorithm that is resilient to delayed updates. Our extensive evaluation shows that Online FL, as implemented by FLeet, can deliver a 2.3x quality boost compared to Standard FL, while only consuming 0.036% of the battery per day. I-Prof can accurately control the impact of learning tasks by improving the prediction accuracy up to 3.6x (computation time) and up to 19x (energy). AdaSGD outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.