Revisiting Ensembling in One-Shot Federated Learning

Youssef Allouah; Akash Dhasade; Rachid Guerraoui; Nirupam Gupta; Anne-Marie Kermarrec; Rafael Pinot; Rafael Pires; Rishi Sharma

arXiv · 2411.07182

Revisiting Ensembling in One-Shot Federated Learning

Youssef Allouah·Akash Dhasade·Rachid Guerraoui·Nirupam Gupta·Anne-Marie Kermarrec·Rafael Pinot·Rafael Pires·Rishi Sharma

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Abstract

Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades the iterative exchange of models between clients and the server with a single round of communication, thereby saving substantially on communication costs. Not surprisingly, OFL exhibits a performance gap in terms of accuracy with respect to FL, especially under high data heterogeneity. We introduce FENS, a novel federated ensembling scheme that approaches the accuracy of FL with the communication efficiency of OFL. Learning in FENS proceeds in two phases: first, clients train models locally and send them to the server, similar to OFL; second, clients collaboratively train a lightweight prediction aggregator model using FL. We showcase the effectiveness of FENS through exhaustive experiments spanning several datasets and heterogeneity levels. In the particular case of heterogeneously distributed CIFAR-10 dataset, FENS achieves up to a 26.9% higher accuracy over state-of-the-art (SOTA) OFL, being only 3.1% lower than FL. At the same time, FENS incurs at most 4.3x more communication than OFL, whereas FL is at least 10.9x more communication-intensive than FENS.

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Youssef Allouah, Akash Dhasade, Rachid Guerraoui, Nirupam Gupta, Anne-Marie Kermarrec, Rafael Pinot, Rafael Pires, Rishi Sharma. 2024-11-11. Revisiting Ensembling in One-Shot Federated Learning. https://arxiv.org/abs/2411.07182

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