FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning

Bingjie Yan; Boyi Liu; Lujia Wang; Yize Zhou; Zhixuan Liang; Ming Liu; Cheng Zhong Xu

doi:10.1109/IJCNN52387.2021.9534451

FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning

Bingjie Yan^*, Boyi Liu^*, Lujia Wang^*, Yize Zhou, Zhixuan Liang, Ming Liu, Cheng Zhong Xu

^*Corresponding author for this work

Department of Computer Science and Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

28 Citations (Scopus)

Abstract

Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every round, which enable it to perform in real-time. Real-time is not considered by the existing approaches, but it is critical for FL systems to allocate computing power, communication resources, etc. Compared to the state-of-the-art method, the experimental results show that FedCM is more sensitive to data quantity and data quality under the premise of real-time. Furthermore, we developed federated learning open-source software based on FedCM. The software has been applied to identify COVID-19 based on medical images.

Original language	English
Title of host publication	IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780738133669
DOIs	https://doi.org/10.1109/IJCNN52387.2021.9534451
Publication status	Published - 18 Jul 2021
Event	2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China Duration: 18 Jul 2021 → 22 Jul 2021

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2021-July

Conference

Conference	2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/Territory	China
City	Virtual, Shenzhen
Period	18/07/21 → 22/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

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10.1109/IJCNN52387.2021.9534451

Cite this

Yan, B., Liu, B., Wang, L., Zhou, Y., Liang, Z., Liu, M., & Xu, C. Z. (2021). FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN52387.2021.9534451

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title = "FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning",

abstract = "Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every round, which enable it to perform in real-time. Real-time is not considered by the existing approaches, but it is critical for FL systems to allocate computing power, communication resources, etc. Compared to the state-of-the-art method, the experimental results show that FedCM is more sensitive to data quantity and data quality under the premise of real-time. Furthermore, we developed federated learning open-source software based on FedCM. The software has been applied to identify COVID-19 based on medical images.",

author = "Bingjie Yan and Boyi Liu and Lujia Wang and Yize Zhou and Zhixuan Liang and Ming Liu and Xu, \{Cheng Zhong\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",

year = "2021",

month = jul,

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doi = "10.1109/IJCNN52387.2021.9534451",

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Yan, B, Liu, B, Wang, L, Zhou, Y, Liang, Z, Liu, M & Xu, CZ 2021, FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Shenzhen, China, 18/07/21. https://doi.org/10.1109/IJCNN52387.2021.9534451

FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning. / Yan, Bingjie; Liu, Boyi; Wang, Lujia et al.
IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - FedCM

T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021

AU - Yan, Bingjie

AU - Liu, Boyi

AU - Wang, Lujia

AU - Zhou, Yize

AU - Liang, Zhixuan

AU - Liu, Ming

AU - Xu, Cheng Zhong

PY - 2021/7/18

Y1 - 2021/7/18

N2 - Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every round, which enable it to perform in real-time. Real-time is not considered by the existing approaches, but it is critical for FL systems to allocate computing power, communication resources, etc. Compared to the state-of-the-art method, the experimental results show that FedCM is more sensitive to data quantity and data quality under the premise of real-time. Furthermore, we developed federated learning open-source software based on FedCM. The software has been applied to identify COVID-19 based on medical images.

AB - Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every round, which enable it to perform in real-time. Real-time is not considered by the existing approaches, but it is critical for FL systems to allocate computing power, communication resources, etc. Compared to the state-of-the-art method, the experimental results show that FedCM is more sensitive to data quantity and data quality under the premise of real-time. Furthermore, we developed federated learning open-source software based on FedCM. The software has been applied to identify COVID-19 based on medical images.

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DO - 10.1109/IJCNN52387.2021.9534451

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Yan B, Liu B, Wang L, Zhou Y, Liang Z, Liu M et al. FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN52387.2021.9534451

FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning

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