What Do We Mean By Generalization In Federated Learning? - ArXiv

Computer Science > Machine Learning arXiv:2110.14216 (cs) [Submitted on 27 Oct 2021 (v1), last revised 16 Mar 2022 (this version, v2)] Title:What Do We Mean by Generalization in Federated Learning? Authors:Honglin Yuan, Warren Morningstar, Lin Ning, Karan Singhal View a PDF of the paper titled What Do We Mean by Generalization in Federated Learning?, by Honglin Yuan and 3 other authors View PDF

Abstract:Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps from unseen client distributions (participation gap). In this work, we propose a framework for disentangling these performance gaps. Using this framework, we observe and explain differences in behavior across natural and synthetic federated datasets, indicating that dataset synthesis strategy can be important for realistic simulations of generalization in federated learning. We propose a semantic synthesis strategy that enables realistic simulation without naturally-partitioned data. Informed by our findings, we call out community suggestions for future federated learning works.

Comments:	Accepted to ICLR 2022. Code repository see this https URL
Subjects:	Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML)
Cite as:	arXiv:2110.14216 [cs.LG]
(or arXiv:2110.14216v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2110.14216 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Honglin Yuan [view email] [v1] Wed, 27 Oct 2021 07:01:14 UTC (9,773 KB) [v2] Wed, 16 Mar 2022 05:54:12 UTC (7,223 KB) Full-text links:

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