FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

• URL: http://arxiv.org/abs/2006.07273v2
• Date: Thu, 3 Dec 2020 11:19:50 GMT
• Title: FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction
• Authors: Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec, Vlad Nitu, Rhicheek Patra, Francois Taiani
• Abstract summary: This paper presents FLeet, the first Online Federated Learning system. Online FL combines the privacy of Standard FL with the precision of online learning. I-Prof is a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices. AdaSGD is a new adaptive learning algorithm that is resilient to delayed updates.
• Score: 9.408271687085476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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