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Volume 39 Issue 3
July  2024
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Xu Y, Xiao MJ, Wu C et al. Age-of-Information-Aware federated learning. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(3): 637−653 May 2024. DOI: 10.1007/s11390-024-3914-x.
Citation: Xu Y, Xiao MJ, Wu C et al. Age-of-Information-Aware federated learning. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(3): 637−653 May 2024. DOI: 10.1007/s11390-024-3914-x.
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Age-of-Information-Aware Federated Learning

  • Yin Xu1, 2,  (徐殷) Student Member, CCF, 
  • Ming-Jun Xiao1, 2, 3, ,  (肖明军) Member, CCF, IEEE, 
  • Chen Wu3,  (吴晨) , 
  • Jie Wu4,  (吴杰) Fellow, IEEE, 
  • Jin-Rui Zhou1, 2,  (周津锐) Student Member, CCF, 
  • He Sun1, 2,  (孙贺) Student Member, CCF
  • 1.

    School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China

  • 2.

    Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China

  • 3.

    School of Data Science, University of Science and Technology of China, Hefei 230026, China

  • 4.

    Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, U.S.A.

Funds: This work was supported by the National Natural Science Foundation of China under Grant No. 62172386, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20231212, the Teaching Research Project of the Education Department of Anhui Province of China under Grant No. 2021jyxm1738.
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  • Author Bio:

    Yin Xu received her B.S. degree from the Anhui University (AHU), Hefei, in 2019. She is currently a Ph.D. candidate in the School of Computer Science and Technology at the University of Science and Technology of China (USTC), Hefei. Her research interests include crowd computing, federated learning, privacy preservation, game theory, edge computing, and incentive mechanism design

    Ming-Jun Xiao is a professor in the School of Computer Science and Technology at the University of Science and Technology of China (USTC), Hefei. He received his Ph.D. degree from USTC, Hefei, in 2004. His research interests include mobile crowdsensing, edge computing, federated learning, auction theory, and data privacy

    Chen Wu received his B.S. degree from the University of Science and Technology of China (USTC), Hefei, in 2021. He is currently a master student in the School of Data Science at USTC, Hefei. His research interests include crowdsensing, federated learning, and applied statistics

    Jie Wu is the Director of the Center for Networked Computing and Laura H. Carnell professor at Temple University, Philadelphia. He also serves as the Director of International Affairs at College of Science and Technology. His current research interests include mobile computing and wireless networks, routing protocols, network trust and security, distributed algorithms, applied machine learning, and cloud computing. Dr. Wu is a Fellow of the AAAS and a Fellow of IEEE. He is the recipient of the 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award

    Jin-Rui Zhou received his B.S. degree from the University of Science and Technology of China (USTC), Hefei, in 2021. He is currently a master student in the School of Computer Science and Technology at USTC, Hefei. His research interests include crowdsensing, federated learning, sequential decision-making, online learning, and applied statistics

    He Sun received his B.S. and B.A. degrees from Qingdao University, Qingdao, in 2020. He is currently working toward his Ph.D. degree in the School of Computer Science and Technology at University of Science and Technology of China, Hefei. His research interests include reinforcement learning and privacy preservation

  • Corresponding author:

    xiaomj@ustc.edu.cn

  • Received Date: November 01, 2023
  • Accepted Date: February 05, 2024
    Abstract

  • Federated learning (FL) is an emerging privacy-preserving distributed computing paradigm, enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’ private datasets to the central server. Unlike most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process, our study addresses such scenarios in this paper where clients’ datasets need to be updated periodically, and the server can incentivize clients to employ as fresh as possible datasets for local model training. Our primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained budget. To this end, we introduce the concept of ‘‘Age of Information’’ (AoI) to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL system. Based on the convergence bound, we further formulate our problem as a restless multi-armed bandit (RMAB) problem. Next, we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple subproblems. Finally, we propose a Whittle’s Index Based Client Selection (WICS) algorithm to determine the set of selected clients. In addition, comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.

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