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2021 | OriginalPaper | Buchkapitel

UCBFed: Using Reinforcement Learning Method to Tackle the Federated Optimization Problem

verfasst von : Wanqi Chen, Xin Zhou

Erschienen in: Distributed Applications and Interoperable Systems

Verlag: Springer International Publishing

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Abstract

Federated learning is a novel research area of AI technology that focus on distributed training and privacy preservation. Current federated optimization algorithms face serious challenge in the aspects of speed and accuracy, especially in non-i.i.d scenario. In this work, we propose UCBFed, a federated optimization algorithm that uses the Upper Confidence Bound (UCB) method to heuristically select participating clients in each round’s optimization process. We evaluate our algorithm in multiple federated distributed datasets. Comparing to most widely-used FedAvg and FedOpt, the UCBFed we proposed is superior in both the final accuracy and communication efficiency.
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Metadaten
Titel
UCBFed: Using Reinforcement Learning Method to Tackle the Federated Optimization Problem
verfasst von
Wanqi Chen
Xin Zhou
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-78198-9_7

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