Multi-Armed Bandit-Based Client Scheduling for Federated Learning
By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and uplo...
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| Veröffentlicht in: | IEEE transactions on wireless communications Jg. 19; H. 11; S. 7108 - 7123 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1536-1276, 1558-2248 |
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| Abstract | By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms. |
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| AbstractList | By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms. |
| Author | Yang, Howard H. Xia, Wenchao Quek, Tony Q. S. Zhu, Hongbo Guo, Kun Wen, Wanli |
| Author_xml | – sequence: 1 givenname: Wenchao orcidid: 0000-0001-6245-0347 surname: Xia fullname: Xia, Wenchao email: wenchao_xia@sutd.edu.sg organization: Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore – sequence: 2 givenname: Tony Q. S. orcidid: 0000-0002-4037-3149 surname: Quek fullname: Quek, Tony Q. S. email: tonyquek@sutd.edu.sg organization: Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore – sequence: 3 givenname: Kun orcidid: 0000-0003-1251-3578 surname: Guo fullname: Guo, Kun email: kun_guo@sutd.edu.sg organization: Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore – sequence: 4 givenname: Wanli orcidid: 0000-0003-3810-8466 surname: Wen fullname: Wen, Wanli email: wanli_wen@sutd.edu.sg organization: Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore – sequence: 5 givenname: Howard H. orcidid: 0000-0002-0256-2416 surname: Yang fullname: Yang, Howard H. email: howard_yang@sutd.edu.sg organization: Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore – sequence: 6 givenname: Hongbo surname: Zhu fullname: Zhu, Hongbo email: zhuhb@njupt.edu.cn organization: Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, China |
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| Cites_doi | 10.1561/2200000024 10.1109/ALLERTON.2009.5394517 10.1109/ICASSP40776.2020.9053740 10.1109/TNET.2011.2181864 10.1109/ICASSP40776.2020.9054634 10.1080/10556788.2016.1278445 10.1109/TWC.2019.2961673 10.2307/3214163 10.1109/JSAC.2019.2904348 10.1023/A:1013689704352 10.1109/TWC.2019.2946245 10.1109/TSP.2020.2981904 10.1109/TCOMM.2019.2944169 10.1016/0196-8858(85)90002-8 10.1109/ICC.2019.8761315 10.1109/ITA.2018.8503124 10.1109/INFOCOM.2019.8737461 10.1109/TSP.2020.2983166 10.2200/S00271ED1V01Y201006CNT007 |
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| SubjectTerms | Algorithms client scheduling client selection Clients Communication Computational modeling Confidence Convergence Data models Datasets Federated learning multi-armed bandit (MAB) Multi-armed bandit problems Scheduling Smart phones Training Upper bound Upper bounds Wireless communication |
| Title | Multi-Armed Bandit-Based Client Scheduling for Federated Learning |
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