Adaptive federated learning aggregation strategies based on mobile edge computing

Shucheng Liu; Fangqin Xu

doi:10.1117/12.2675238

25 May 2023 Adaptive federated learning aggregation strategies based on mobile edge computing

Shucheng Liu, Fangqin Xu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360C (2023) https://doi.org/10.1117/12.2675238
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Federated learning is a machine learning paradigm that can protect data privacy, but the high communication cost and the arithmetic limitation of clients become one of the bottlenecks of federated learning. To address this problem, an adaptive federated learning aggregation strategy based on mobile edge computing called A-FedAvg is proposed, which first evaluates the accuracy gain metrics and cost metrics from each round of global iteration in federated learning, and then combines them into an adaptive metric by weighting parameters to indicate the number of local iterations needed for the next round of global iteration. The A-FedAvg algorithm is designed and then experimented on the MNIST and EMNIST datasets for IID and non-IID data distributions, respectively. The results show that the A-FedAvg algorithm can reduce the number of global communications compared to the FedAvg algorithm with guaranteed accuracy and without affecting the normal use of the mobile device client.

Citation Download Citation

Shucheng Liu and Fangqin Xu "Adaptive federated learning aggregation strategies based on mobile edge computing", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360C (25 May 2023); https://doi.org/10.1117/12.2675238

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KEYWORDS

Machine learning

Education and training

Data modeling

Instrument modeling

Mathematical optimization

Deep learning

Mobile communications

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