[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Age-of-Information-Aware Federated Learning

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Yang Q, Liu Y, Chen T T, Tong Y X. Federated machine learning: Concept and applications. ACM Trans. Intelligent Systems and Technology, 2019, 10(2): Article No. 12. DOI: https://doi.org/10.1145/3298981.

  2. Li T, Sahu A K, Talwalkar A, Smith V. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 2020, 37(3): 50–60. DOI: https://doi.org/10.1109/MSP.2020.2975749.

    Article  Google Scholar 

  3. Shi Y F, Liu Y Q, Wei K, Shen L, Wang X Q, Tao D C. Make landscape flatter in differentially private federated learning. In Proc. the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2023, pp.24552–24562. DOI: https://doi.org/10.1109/CVPR52729.2023.02352.

  4. Antunes R S, da Costa C A, Küderle A, Yari I A, Eskofier B M. Federated learning for healthcare: Systematic review and architecture proposal. ACM Trans. Intelligent Systems and Technology, 2022, 13(4): Article No. 54. DOI: https://doi.org/10.1145/3501813.

  5. Wang Z B, Huang Y T, Song M K, Wu L B, Xue F, Ren K. Poisoning-assisted property inference attack against federated learning. IEEE Trans. Dependable and Secure Computing, 2023, 20(4): 3328–3340. DOI: https://doi.org/10.1109/TDSC.2022.3196646.

    Article  Google Scholar 

  6. Lim W Y B, Luong N C, Hoang D T, Jiao Y T, Liang Y C, Yang Q, Niyato D, Miao C Y. Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 2020, 22(3): 2031–2063. DOI: https://doi.org/10.1109/COMST.2020.2986024.

    Article  Google Scholar 

  7. Karimireddy S P, Kale S, Mohri M, Reddi S J, Stich S U, Suresh A T. SCAFFOLD: Stochastic controlled averaging for federated learning. In Proc. the 37th International Conference on Machine Learning, Jul. 2020, Article No. 476.

  8. Wu X D, Huang F H, Hu Z M, Huang H. Faster adaptive federated learning. In Proc. the 37th AAAI Conference on Artificial Intelligence, Feb. 2023, pp. 10379–10387. DOI: https://doi.org/10.1609/AAAI.V37I9.26235.

  9. Wang H, Kaplan Z, Niu D, Li B C. Optimizing federated learning on Non-IID data with reinforcement learning. In Proc. the 2020 IEEE Conference on Computer Communications (INFOCOM), Jul. 2020, pp.1698–1707. DOI: https://doi.org/10.1109/INFOCOM41043.2020.9155494.

  10. Chen C, Xu H, Wang W, Li B C, Li B, Chen L, Zhang G. Communication-efficient federated learning with adaptive parameter freezing. In Proc. the 41st IEEE International Conference on Distributed Computing Systems (ICDCS), Jul. 2021, pp.1–11. DOI: https://doi.org/10.1109/ICDCS51616.2021.00010.

  11. Wang Z B, Ma J J, Wang X, Hu J H, Qin Z, Ren K. Threats to training: A survey of poisoning attacks and defenses on machine learning systems. ACM Computing Surveys, 2023, 55(7): 134. DOI: https://doi.org/10.1145/3538707.

    Article  Google Scholar 

  12. Zhang X L, Li F T, Zhang Z Y, Li Q, Wang C, Wu J P. Enabling execution assurance of federated learning at untrusted participants. In Proc. the 2020 IEEE Conference on Computer Communications (INFOCOM), Jul. 2020, pp.1877–1886. DOI: https://doi.org/10.1109/INFOCOM41043.2020.9155414.

  13. Yuan X Y, Ma X Y, Zhang L, Fang Y G, Wu D P. Beyond class-level privacy leakage: Breaking record-level privacy in federated learning. IEEE Internet of Things Journal, 2022, 9(4): 2555–2565. DOI: https://doi.org/10.1109/JIOT.2021.3089713.

    Article  Google Scholar 

  14. Tran N H, Bao W, Zomaya A, Nguyen M N H, Hong C S. Federated learning over wireless networks: Optimization model design and analysis. In Proc. the 2019 IEEE Conference on Computer Communications (INFOCOM), Apr. 2019, pp.1387–1395. DOI: https://doi.org/10.1109/INFOCOM.2019.8737464.

  15. Wang Z L, Hu Q, Li R N, Xu M H, Xiong Z H. Incentive mechanism design for joint resource allocation in blockchain-based federated learning. IEEE Trans. Parallel and Distributed Systems, 2023, 34(5): 1536–1547. DOI: https://doi.org/10.1109/TPDS.2023.3253604.

    Article  Google Scholar 

  16. Zhan Y F, Li P, Qu Z H, Zeng D Z, Guo S. A learningbased incentive mechanism for federated learning. IEEE Internet of Things Journal, 2020, 7(7): 6360–6368. DOI: https://doi.org/10.1109/JIOT.2020.2967772.

    Article  Google Scholar 

  17. Zeng R F, Zhang S X, Wang J Q, Chu X W. FMore: An incentive scheme of multi-dimensional auction for federated learning in MEC. In Proc. the 40th IEEE International Conference on Distributed Computing Systems (ICDCS), Nov. 29–Dec. 1, 2020, pp.278–288. DOI: https://doi.org/10.1109/ICDCS47774.2020.00094.

  18. Kaul S, Gruteser M, Rai V, Kenney J. Minimizing age of information in vehicular networks. In Proc. the 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Jun. 2011, pp.350–358. DOI: https://doi.org/10.1109/SAHCN.2011.5984917.

  19. Wu C, Xiao M J, Wu J, Xu Y, Zhou J R, Sun H. Towards federated learning on fresh datasets. In Proc. the 20th IEEE International Conference on Mobile Ad Hoc and Smart Systems (MASS), Sept. 2023, pp.320–328. DOI: https://doi.org/10.1109/MASS58611.2023.00046.

  20. Xu Y, Xiao M J, Wu J, Tan H S, Gao G J. A personalized privacy preserving mechanism for crowdsourced federated learning. IEEE Trans. Mobile Computing, 2024, 23(2): 1568–1585. DOI: https://doi.org/10.1109/TMC.2023.3237636.

    Google Scholar 

  21. Zhou Y P, Liu X Z, Fu Y, Wu D, Wang J H, Yu S. Optimizing the numbers of queries and replies in convex federated learning with differential privacy. IEEE Trans. Dependable and Secure Computing, 2023, 20(6): 4823–4837. DOI: https://doi.org/10.1109/TDSC.2023.3234599.

    Article  Google Scholar 

  22. Hosmer D W, Lemeshow S. Applied Logistic Regression (2nd edition). Wiley, 2000. DOI: https://doi.org/10.1002/0471722146.

  23. Hearst M A, Dumais S T, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intelligent Systems and their Applications, 1998, 13(4): 18–28. DOI: https://doi.org/10.1109/5254.708428.

    Article  Google Scholar 

  24. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84–90. DOI: https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  25. McCall B P. Multi-armed bandit allocation indices (J. C. Gittins). SIAM Review, 1991, 33(1): 154–155. DOI: https://doi.org/10.1137/1033039.

    Article  Google Scholar 

  26. Xu Y, Xiao M J, Wu J, Zhang S, Gao G J. Incentive mechanism for spatial crowdsourcing with unknown social-aware workers: A three-stage stackelberg game approach. IEEE Trans. Mobile Computing, 2023, 22(8): 4698–4713. DOI: https://doi.org/10.1109/TMC.2022.3157687.

    Article  Google Scholar 

  27. Whittle P. Restless bandits: Activity allocation in a changing world. Journal of Applied Probability, 1988, 25(A): 287–298. DOI: https://doi.org/10.2307/3214163.

    Article  MathSciNet  Google Scholar 

  28. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278–2324. DOI: https://doi.org/10.1109/5.726791.

    Article  Google Scholar 

  29. Xiao H, Rasul K, Vollgraf R. Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms, arXiv: 1708.07747, 2017. http://arxiv.org/abs/1708.07747, May 2024.

  30. Wang S Q, Tuor T, Salonidis T, Leung K K, Makaya C, He T, Chan K. When edge meets learning: Adaptive control for resource-constrained distributed machine learning. In Proc. the 2018 IEEE Conference on Computer Communications (INFOCOM), Apr. 2018, pp.63–71. DOI: https://doi.org/10.1109/INFOCOM.2018.8486403.

  31. Yang H H, Arafa A, Quek T Q S, Poor H V. Age-based scheduling policy for federated learning in mobile edge networks. In Proc. the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, pp.8743–8747. DOI: https://doi.org/10.1109/ICASSP40776.2020.9053740.

  32. Yang K, Jiang T, Shi Y M, Ding Z. Federated learning via over-the-air computation. IEEE Trans. Wireless Communications, 2020, 19(3): 2022–2035. DOI: https://doi.org/10.1109/TWC.2019.2961673.

    Article  Google Scholar 

  33. Wang E, Zhang M J, Yang B, Yang Y J, Wu J. Large-scale spatiotemporal fracture data completion in sparse crowdSensing. IEEE Trans. Mobile Computing, 2023. DOI: https://doi.org/10.1109/TMC.2023.3339089.

  34. Wang Z B, Hu J H, Lv R Z, Wei J, Wang Q, Yang D J, Qi H R. Personalized privacy-preserving task allocation for mobile crowdsensing. IEEE Trans. Mobile Computing, 2019, 18(6): 1330–1341. DOI: https://doi.org/10.1109/TMC.2018.2861393.

    Article  Google Scholar 

  35. Lai F, Zhu X F, Madhyastha H V, Chowdhury M. Oort: Efficient federated learning via guided participant selection. In Proc. the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2021, pp.19–35.

  36. Xu Y, Xiao M J, Tan H S, Liu A, Gao G J, Yan Z Y. Incentive mechanism for differentially private federated learning in industrial internet of things. IEEE Trans. Industrial Informatics, 2022, 18(10): 6927–6939. DOI: https://doi.org/10.1109/TII.2021.3134257.

    Article  Google Scholar 

  37. Lee H, Lee J, Kim H, Pack S. Straggler-aware in-network aggregation for accelerating distributed deep learning. IEEE Trans. Services Computing, 2023, 16(6): 4198–4204. DOI: https://doi.org/10.1109/TSC.2023.3309318.

    Article  Google Scholar 

  38. Dai Z P, Wang H, Liu C H, Han R, Tang J, Wang G R. Mobile crowdsensing for data freshness: A deep reinforcement learning approach. In Proc. the 2021 IEEE Conference on Computer Communications (INFOCOM), May 2021. DOI: https://doi.org/10.1109/INFOCOM42981.2021.9488791.

  39. Tripathi V, Modiano E. Age debt: A general framework for minimizing age of information. In Proc. the 2021 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), May 2021. DOI: https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484621.

  40. Fang M B, Wang X J, Xu C, Yang H H, Quek T Q S. Computing-aided update for information freshness in the internet of things. In Proc. the 2021 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), May 2021. DOI: https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484521.

  41. Tang H Y, Wang J T, Song L Q, Song J. Minimizing age of information with power constraints: Multi-user opportunistic scheduling in multi-state time-varying channels. IEEE Journal on Selected Areas in Communications, 2020, 38(5): 854–868. DOI: https://doi.org/10.1109/JSAC.2020.2980911.

    Article  Google Scholar 

  42. Xu Y, Xiao M J, Zhu Y, Wu J, Zhang S, Zhou J R. Aoiguaranteed incentive mechanism for mobile crowdsensing with freshness concerns. IEEE Trans. Mobile Computing, 2024, 23(5): 4107–4125. DOI: https://doi.org/10.1109/TMC.2023.3285779.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Yin Xu  (徐　殷), Ming-Jun Xiao  (肖明军), Jin-Rui Zhou  (周津锐) & He Sun  (孙　贺)

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

    Yin Xu  (徐　殷), Ming-Jun Xiao  (肖明军), Jin-Rui Zhou  (周津锐) & He Sun  (孙　贺)

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

    Ming-Jun Xiao  (肖明军) & Chen Wu  (吴　晨)

  4. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, 19122, USA

    Jie Wu  (吴　杰)

Authors
  1. Yin Xu  (徐　殷)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming-Jun Xiao  (肖明军)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Wu  (吴　晨)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Wu  (吴　杰)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin-Rui Zhou  (周津锐)
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. He Sun  (孙　贺)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming-Jun Xiao  (肖明军).

Ethics declarations

Conflict of Interest Jie Wu is an editorial board member for Journal of Computer Science and Technology and was not involved in the editorial review of this article. All authors declare that there are no other competing interests.

Additional information

A preliminary version of this paper was published in the Proceedings of MASS 2023.

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.

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 decisionmaking, 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.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, Y., Xiao, MJ., Wu, C. et al. Age-of-Information-Aware Federated Learning. J. Comput. Sci. Technol. 39, 637–653 (2024). https://doi.org/10.1007/s11390-024-3914-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-024-3914-x

Keywords

Search

Navigation