Abstract
Federated learning enables multiple data owners to collaboratively train a shared machine learning model without the need to disclose their local training data. However, it is not practical for all clients to unconditionally contribute their resources; thus, designing an incentive mechanism in federated learning becomes an important issue. Some existing studies adopt the framework of reverse auctions for incentive design, but they do not take the uncertainty of the training time of clients into account. Consequently, a situation may arise where a client is unable to meet the training deadline, and the server cannot wait indefinitely. In this paper, we propose a reverse auction framework that takes the uncertainty of training time into account. We formulate an expected social welfare maximization problem and prove its NP-hardness. We then introduce an efficient dynamic programming-based algorithm which can find an optimal solution in pseudo-polynomial time. Building upon this, we propose a truthful auction mechanism based on the well-known Vickrey-Clarke-Groves (VCG) mechanism. Furthermore, to reduce the time complexity, we introduce an additional truthful auction mechanism based on a greedy algorithm which achieves a near-optimal performance in polynomial time. Finally, the effectiveness of the proposed two auction mechanisms is verified through simulation experiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, K., Tasnim, S., Yoshii, K.: Simulation of auction mechanism model for energy-efficient high performance computing. In: Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp. 99–104 (2020)
Bonawitz, K., et al.: Towards federated learning at scale: system design. Proc. Mach. Learn. Syst. 1, 374–388 (2019)
Carleo, G., et al.: Machine learning and the physical sciences. Rev. Mod. Phys. 91(4), 045002 (2019)
Coleman, C., et al.: Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark. ACM SIGOPS Oper. Syst. Rev. 53(1), 14–25 (2019)
Deng, Y., et al.: Fair: quality-aware federated learning with precise user incentive and model aggregation. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1–10. IEEE (2021)
Duan, Y., Wang, N., Wu, J.: Minimizing training time of distributed machine learning by reducing data communication. IEEE Trans. Netw. Sci. Eng. 8(2), 1802–1814 (2021)
Dütting, P., Henzinger, M., Starnberger, M.: Valuation compressions in VCG-based combinatorial auctions. ACM Trans. Econ. Comput. (TEAC) 6(2), 1–18 (2018)
Ghosh, A., Chung, J., Yin, D., Ramchandran, K.: An efficient framework for clustered federated learning. Adv. Neural. Inf. Process. Syst. 33, 19586–19597 (2020)
Gu, Y., Hou, D., Wu, X., Tao, J., Zhang, Y.: Decentralized transaction mechanism based on smart contract in distributed data storage. Information 9(11), 286 (2018)
Hu, R., Gong, Y.: Trading data for learning: incentive mechanism for on-device federated learning. In: GLOBECOM 2020–2020 IEEE Global Communications Conference, pp. 1–6. IEEE (2020)
Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Trans. Mob. Comput. 20(10), 3034–3048 (2020)
Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends® Mach. Learn. 14(1–2), 1–210 (2021)
Kang, J., Xiong, Z., Niyato, D., Xie, S., Zhang, J.: Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6(6), 10700–10714 (2019)
Kim, H., Park, J., Bennis, M., Kim, S.L.: Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2019)
Kravets, P., et al.: Markovian learning methods in decision-making systems. In: Babichev, S., Lytvynenko, V. (eds.) ISDMCI 2021. LNDECT, vol. 77, pp. 423–437. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-82014-5_28
Le, T.H.T., et al.: An incentive mechanism for federated learning in wireless cellular networks: an auction approach. IEEE Trans. Wireless Commun. 20(8), 4874–4887 (2021)
Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D.: Machine learning in agriculture: a review. Sensors 18(8), 2674 (2018)
Lim, W.Y.B., et al.: Hierarchical incentive mechanism design for federated machine learning in mobile networks. IEEE Internet Things J. 7(10), 9575–9588 (2020)
Mohri, M., Sivek, G., Suresh, A.T.: Agnostic federated learning. In: International Conference on Machine Learning, pp. 4615–4625. PMLR (2019)
Roughgarden, T.: Algorithmic game theory. Commun. ACM 53(7), 78–86 (2010)
Tarca, A.L., Carey, V.J., Chen, X.W., Romero, R., Drăghici, S.: Machine learning and its applications to biology. PLoS Computat. Biol. 3(6), e116 (2007)
Tran, N.H., Bao, W., Zomaya, A., Nguyen, M.N., Hong, C.S.: Federated learning over wireless networks: optimization model design and analysis. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 1387–1395. IEEE (2019)
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging. arXiv preprint arXiv:2002.06440 (2020)
Wu, M., Ye, D., Ding, J., Guo, Y., Yu, R., Pan, M.: Incentivizing differentially private federated learning: a multidimensional contract approach. IEEE Internet Things J. 8(13), 10639–10651 (2021)
Yan, F., Ruwase, O., He, Y., Chilimbi, T.: Performance modeling and scalability optimization of distributed deep learning systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1355–1364 (2015)
Zeng, R., Zhang, S., Wang, J., Chu, X.: FMore: an incentive scheme of multi-dimensional auction for federated learning in MEC. In: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), pp. 278–288. IEEE (2020)
Zhan, Y., Li, P., Qu, Z., Zeng, D., Guo, S.: A learning-based incentive mechanism for federated learning. IEEE Internet Things J. 7(7), 6360–6368 (2020)
Acknowledgements
This paper is supported by the Fundamental Research Funds for the Central Universities under Grant No. B210201053, the National Natural Science Foundation of China under Grant No. 61832005, and the Future Network Scientific Research Fund Project under Grant No. FNSRFP-2021-ZD-07.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xu, J., Tang, B., Cui, H., Ye, B. (2024). An Uncertainty-Aware Auction Mechanism for Federated Learning. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14492. Springer, Singapore. https://doi.org/10.1007/978-981-97-0811-6_1
Download citation
DOI: https://doi.org/10.1007/978-981-97-0811-6_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0810-9
Online ISBN: 978-981-97-0811-6
eBook Packages: Computer ScienceComputer Science (R0)