An efficient federated learning optimization algorithm on non-IID data

Xue Wang

doi:10.1117/12.2640939

13 October 2022 An efficient federated learning optimization algorithm on non-IID data

Xue Wang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870C (2022) https://doi.org/10.1117/12.2640939
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Recently, federated learning has received a lot of attention. It is a novel distributed machine learning scheme that enables a large number of edge computing devices to collaboratively learn and train models without leaking local data. Federated learning can break down data barriers, effectively solve the problem of data island, and is committed to ensuring user privacy and data security, and has received extensive attention from industry and academia. However, in practical industrial scenarios, federated learning needs to consider more effects of heterogeneity than traditional machine learning. Aiming at the problems of high communication cost and heterogeneous client data in federated learning, this paper proposes a federated learning training method based on knowledge distillation, which realizes the sample-level information exchange between the server and the client, and guides each client to train local personality. The model is optimized to reduce the impact of heterogeneous data on the client side, and the distillation loss is used to ensure the stability of the model performance. Theoretical analysis and experimental results show that the federated learning algorithm proposed in this paper can ensure the data privacy and security of participants. And in the scenario of Non-IID data, the algorithm can improve the training efficiency and reduce the system communication cost on the premise of ensuring the accuracy of the model, and has the feasibility of practical industrial application.

Citation Download Citation

Xue Wang "An efficient federated learning optimization algorithm on non-IID data", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870C (13 October 2022); https://doi.org/10.1117/12.2640939

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KEYWORDS

Data modeling

Performance modeling

Instrument modeling

Machine learning

Data communications

Evolutionary algorithms

Neural networks

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