Poster
Revisiting Ensembling in One-Shot Federated Learning
Youssef Allouah · Akash Dhasade · Rachid Guerraoui · Nirupam Gupta · Anne-marie Kermarrec · Rafael Pinot · Rafael Pires · Rishi Sharma
West Ballroom A-D #6000
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Abstract
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Wed 11 Dec 4:30 p.m. PST
— 7:30 p.m. PST
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 FL (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 SOTA OFL, being only $3.1$% lower than FL. At the same time, Fens incurs at most $4.3\times$ more communication than OFL, whereas FL is at least $10.9\times$ more communication-intensive than Fens.
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