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Computer Science > Machine Learning

arXiv:2109.02388 (cs)
[Submitted on 6 Sep 2021]

Title:On Second-order Optimization Methods for Federated Learning

Authors:Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich
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Abstract:We consider federated learning (FL), where the training data is distributed across a large number of clients. The standard optimization method in this setting is Federated Averaging (FedAvg), which performs multiple local first-order optimization steps between communication rounds. In this work, we evaluate the performance of several second-order distributed methods with local steps in the FL setting which promise to have favorable convergence properties.
We (i) show that FedAvg performs surprisingly well against its second-order competitors when evaluated under fair metrics (equal amount of local computations)-in contrast to the results of previous work. Based on our numerical study, we propose (ii) a novel variant that uses second-order local information for updates and a global line search to counteract the resulting local specificity.
Comments: ICML 2021 Workshop "Beyond first-order methods in ML systems"
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.02388 [cs.LG]
  (or arXiv:2109.02388v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.02388

Submission history

From: Sebastian Bischoff [view email]
[v1] Mon, 6 Sep 2021 12:04:08 UTC (253 KB)
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