AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning

The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates a new business chance for building data market. However, in practical FL scenarios, the hardware conditions and data resources of the partici...

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Vydáno v:	IEEE transactions on parallel and distributed systems Ročník 33; číslo 8; s. 1996 - 2009
Hlavní autoři:	Deng, Yongheng, Lyu, Feng, Ren, Ju, Wu, Huaqing, Zhou, Yuezhi, Zhang, Yaoxue, Shen, Xuemin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Budgets client selection Clients Coders Collaborative work Data integrity Data models Data privacy data quality Design factors Distributed databases distributed system Encoders-Decoders Federated learning Machine learning Neural networks reinforcement learning Task analysis Training
ISSN:	1045-9219, 1558-2183
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Abstract	The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates a new business chance for building data market. However, in practical FL scenarios, the hardware conditions and data resources of the participant clients can vary significantly, leading to different positive/negative effects on the FL performance, where the client selection problem becomes crucial. To this end, we propose AUCTION , an A utomated and q U ality-aware C lient selec TION framework for efficient FL, which can evaluate the learning quality of clients and select them automatically with quality-awareness for a given FL task within a limited budget. To design AUCTION , multiple factors such as data size, data quality, and learning budget that can affect the learning performance should be properly balanced. It is nontrivial since their impacts on the FL model are intricate and unquantifiable. Therefore, AUCTION is designed to encode the client selection policy into a neural network and employ reinforcement learning to automatically learn client selection policies based on the observed client status and feedback rewards quantified by the federated learning performance. In particular, the policy network is built upon an encoder-decoder deep neural network with an attention mechanism, which can adapt to dynamic changes of the number of candidate clients and make sequential client selection actions to reduce the learning space significantly. Extensive experiments are carried out based on real-world datasets and well-known learning models to demonstrate the efficiency, robustness, and scalability of AUCTION .
AbstractList	The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates a new business chance for building data market. However, in practical FL scenarios, the hardware conditions and data resources of the participant clients can vary significantly, leading to different positive/negative effects on the FL performance, where the client selection problem becomes crucial. To this end, we propose AUCTION , an A utomated and q U ality-aware C lient selec TION framework for efficient FL, which can evaluate the learning quality of clients and select them automatically with quality-awareness for a given FL task within a limited budget. To design AUCTION , multiple factors such as data size, data quality, and learning budget that can affect the learning performance should be properly balanced. It is nontrivial since their impacts on the FL model are intricate and unquantifiable. Therefore, AUCTION is designed to encode the client selection policy into a neural network and employ reinforcement learning to automatically learn client selection policies based on the observed client status and feedback rewards quantified by the federated learning performance. In particular, the policy network is built upon an encoder-decoder deep neural network with an attention mechanism, which can adapt to dynamic changes of the number of candidate clients and make sequential client selection actions to reduce the learning space significantly. Extensive experiments are carried out based on real-world datasets and well-known learning models to demonstrate the efficiency, robustness, and scalability of AUCTION .
Author	Ren, Ju Zhang, Yaoxue Shen, Xuemin Lyu, Feng Wu, Huaqing Deng, Yongheng Zhou, Yuezhi
Author_xml	– sequence: 1 givenname: Yongheng orcidid: 0000-0003-3010-3812 surname: Deng fullname: Deng, Yongheng email: dyh19@mails.tsinghua.edu.cn organization: Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, P.R. China – sequence: 2 givenname: Feng orcidid: 0000-0002-2990-5415 surname: Lyu fullname: Lyu, Feng email: fenglyu@csu.edu.cn organization: School of Computer Science and Engineering, Central South University, Changsha, P.R. China – sequence: 3 givenname: Ju orcidid: 0000-0003-2782-183X surname: Ren fullname: Ren, Ju email: renju@tsinghua.edu.cn organization: Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, P.R. China – sequence: 4 givenname: Huaqing orcidid: 0000-0002-3497-6437 surname: Wu fullname: Wu, Huaqing email: h272wu@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 5 givenname: Yuezhi orcidid: 0000-0002-1850-3007 surname: Zhou fullname: Zhou, Yuezhi email: zhouyz@mail.tsinghua.edu.cn organization: Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, P.R. China – sequence: 6 givenname: Yaoxue surname: Zhang fullname: Zhang, Yaoxue email: zhangyx@tsinghua.edu.cn organization: Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, P.R. China – sequence: 7 givenname: Xuemin orcidid: 0000-0002-4140-287X surname: Shen fullname: Shen, Xuemin email: sshen@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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Snippet	The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates...
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SubjectTerms	Artificial neural networks Budgets client selection Clients Coders Collaborative work Data integrity Data models Data privacy data quality Design factors Distributed databases distributed system Encoders-Decoders Federated learning Machine learning Neural networks reinforcement learning Task analysis Training
Title	AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning
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