Abstract
Movited by the modern phenomenon of distributed data collected by edge devices at scale, federated learning can use the large amounts of training data from diverse users for better representation and generalization. To improve flexibility and scalability, we propose a new federated optimization algorithm, named as Multi-index federated aggregation algorithm based on trusted verfication(TVFedmul). TVFedmul is optimized based on Fedavg algorithm, which overcomes a series of problems caused by the original aggregation algorithm, which only takes the single index of data quantity as a reference factor to measure the aggregation weight of each client. The improved aggregation algorithm is based on multi-index measurement, which can reflect the comprehensive ability of clients more comprehensively, so as to make overall judgment. Further, we introduces hyperparameter α, which can be changed to determine the importance of the indexes. Finally, via extensive experimentation, the efficiency and effectiveness of the proposed algorithm is verified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Warnat-Herresthal, S., Schultze, H., Shastry, K.L., et al.: Swarm Learning for decentralized and confidential clinical machine learning. Nature 594(7862), 265–270 (2021)
Jenny, H., Mehryar, M., Theertha, S.A.: FedBoost: communication-efficient algorithms for federated learning. In: International Conference on Machine Learning, pp. 3931–3941 (2020)
Karimireddy, S.P., Kale, S., Mohri, M., et al.: SCAFFOLD: stochastic controlled averaging for on-device federated learning. ArXiv (2019)
Kevin, H., Amar, P., Onur, M., et al.: The Non-IID data quagmire of decentralized machine learning. In: International Conference on Machine Learning, pp. 4337–4348 (2020)
Reisizadeh, A., Mokhtari, A., Hassani, H., et al.: FedPAQ: a communication-efficient federated learning method with periodic averaging and quantization. In: International Conference on Artificial Intelligence and Statistics, vol. 108, pp. 2021–2030 (2020)
Lingjuan, L., Jiangshan, Y., Karthik, N., et al.: Towards fair and privacy-preserving federated deep models. IEEE Trans. Parallel Distribut. Syst. 31, 2524–2541 (2020)
Acar, D.A., Zhao, Y., Navarro, R.M., et al.: Federated learning based on dynamic regularization. In: International Conference on Learning Representations (2021)
McMahan, H.B., Moore, E., Ramage, D., et al.: Communication-efficient learning of deep networks from decentralized data. In: International Conference on Artificial Intelligence and Statistics, vol. 54, pp. 1273–1282 (2017)
Nishio, T., Yonetani, R.: Client selection for federated learning with heterogeneous resources in mobile edge. In: IEEE International Conference on Communications, pp. 1–7 (2019)
Li, L., Xu, W., Chen, T., et al.: RSA: byzantine-robust stochastic aggregation methods for distributed learning fron heterogeneous datasets. In: Proceeding of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1544–1551 (2019)
Zhang, S.X., Choromanska, A., LeCun, Y.: Deep learning with elastic averaging SGD. In: NIPS, vol. 28 (2015)
Chen, M., Mao, B.C., Ma, T.Y.: A staleness-aware asynchronous Federated Learning algorithm with non-IID data. Fut. Generation Comput. Syst. 120, 1–12 (2021)
Li, X., Huang, K., Yang, W., et al.: On the convergence of FedAvg on Non-IID data. Arxiv (2020)
Wei, D., Yi, Z., Nanqing, D., et al.: Toward understanding the impact of stalenessn in distributed machine learning. In: International Conference on Learning Representations (2019)
Xie, C., Koyejo, O., Guptal, I.: Asynchronous federated optimization. ArXiv (2019)
Fallah, A., Mokhtari, A., Ozdaglar, A.: Personalized federated learning: a meta-learning approach. ArXiv (2020)
Li, T., Sanjabi, M., Smith, V.: Fair resource allocation in federated learning. ArXiv (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Bao, Z., Bai, W., Zhang, W. (2022). Multi-index Federated Aggregation Algorithm Based on Trusted Verification. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-96772-7_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96771-0
Online ISBN: 978-3-030-96772-7
eBook Packages: Computer ScienceComputer Science (R0)