Computer Science > Distributed, Parallel, and Cluster Computing
[Submitted on 12 Feb 2020 (v1), last revised 24 Oct 2021 (this version, v2)]
Title:Robustness analytics to data heterogeneity in edge computing
View PDFAbstract:Federated Learning is a framework that jointly trains a model \textit{with} complete knowledge on a remotely placed centralized server, but \textit{without} the requirement of accessing the data stored in distributed machines. Some work assumes that the data generated from edge devices are identically and independently sampled from a common population distribution. However, such ideal sampling may not be realistic in many contexts. Also, models based on intrinsic agency, such as active sampling schemes, may lead to highly biased sampling. So an imminent question is how robust Federated Learning is to biased sampling? In this work\footnote{\url{this https URL}}, we experimentally investigate two such scenarios. First, we study a centralized classifier aggregated from a collection of local classifiers trained with data having categorical heterogeneity. Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge. We present evidence in both scenarios that Federated Learning is robust to data heterogeneity when local training iterations and communication frequency are appropriately chosen.
Submission history
From: Jia Qian [view email][v1] Wed, 12 Feb 2020 15:11:17 UTC (1,473 KB)
[v2] Sun, 24 Oct 2021 15:11:36 UTC (1,433 KB)
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