Federated Learning for Predicting the Next Node in Action FlowsDownload PDF

Published: 21 Oct 2022, Last Modified: 05 May 2023FL-NeurIPS 2022 PosterReaders: Everyone
Keywords: Federated Learning, Personalization, Graph Neural Networks
TL;DR: This paper performs an experimental study of personalized Federated Learning algorithms for the use case of Anonymous company, which uses Graph Neural Networks to predict the next node in action flows.
Abstract: Federated learning is a machine learning approach that allows different clients to collaboratively train a common model without sharing their data sets. Since clients have different data and classify data differently, there is a trade-off between the generality of the common model and the personalization of the classification results. Current approaches rely on using a combination of a global model, common to all clients, and multiple local models, that support personalization. In this paper, we report the results of a study, where we have applied some of these approaches to a concrete use case, namely the Anonymous platform from Anonymous Company, where Graph Neural Networks help programmers in the development of applications. Our results show that the amount of data points of each client affects the personalization strategy and that there is no optimal strategy that fits all clients.
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