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Poster
FetchSGD: Communication-Efficient Federated Learning with Sketching
Daniel Rothchild · Ashwinee Panda · Enayat Ullah · Nikita Ivkin · Ion Stoica · Vladimir Braverman · Joseph Gonzalez · Raman Arora

Tue Jul 14 07:00 AM -- 07:45 AM & Tue Jul 14 06:00 PM -- 06:45 PM (PDT) @ None #None
in Poster Session 1 »

Existing approaches to federated learning suffer from a communication bottleneck as well as convergence issues due to sparse client participation. In this paper we introduce a novel algorithm, called FetchSGD, to overcome these challenges. FetchSGD compresses model updates using a Count Sketch, and then takes advantage of the mergeability of sketches to combine model updates from many workers. A key insight in the design of FetchSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch. This allows the algorithm to move momentum and error accumulation from clients to the central aggregator, overcoming the challenges of sparse client participation while still achieving high compression rates and good convergence. We prove that FetchSGD has favorable convergence guarantees, and we demonstrate its empirical effectiveness by training two residual networks and a transformer model.

Keywords: Large Scale Learning and Big Data · Parallel and Distributed Learning · Other · Optimization - Large Scale, Parallel and Distributed

Author Information

Daniel Rothchild (UC Berkeley)
Ashwinee Panda (UC Berkeley)
Enayat Ullah (Johns Hopkins University)
Nikita Ivkin (Amazon)
Ion Stoica (UC Berkeley)
Vladimir Braverman (Johns Hopkins University)
Joseph Gonzalez (UC Berkeley)
Raman Arora (Johns Hopkins University)

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