Federated learning on non-independent and identically distributed data

Haowei Li; Like Luo; Haolong Wang

doi:10.1117/12.2675255

25 May 2023 Federated learning on non-independent and identically distributed data

Haowei Li, Like Luo, Haolong Wang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360O (2023) https://doi.org/10.1117/12.2675255
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Federated Average algorithm (FEDAVG) is the preferred algorithm for federated learning (FL) because of its simplicity and low communication cost However, if all clients’s local data aren’t independent and equally distributed (that is, nonindependent and identically distributed), FedAvg will have the phenomenon of customer drift, which will lead to the slow convergence speed of the model, and then the efficient cooperative learning to realize the cooperative training of multiple clients will face the challenge of data heterogeneity and vulnerability to attack. This paper systematically summarizes three aspects: local model training improvement, server-side aggregation optimization, and personalized federated learning. The local model can be improved by adjusting the loss function and control variables. Servers can be optimized through asynchronous aggregation, hierarchical aggregation. Personalized federated learning can improve global model performance through both data-based and model-based approaches. This paper puts forward the future research direction of federated learning from all above aspects, and provides reference for the further research of non-IID in federated learning, so as to provide investigation and help for researchers in related fields.

Citation Download Citation

Haowei Li, Like Luo, and Haolong Wang "Federated learning on non-independent and identically distributed data", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360O (25 May 2023); https://doi.org/10.1117/12.2675255

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KEYWORDS

Data modeling

Machine learning

Education and training

Model-based design

Mathematical optimization

Data communications

Evolutionary algorithms

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