Decentralized federated learning privacy protection aggregation

Cong Hu; Ting Lei; Shuang Wang; Zhen Yao; Peng Wang; Tingzeng Zhang

doi:10.1117/12.3005032

16 August 2023 Decentralized federated learning privacy protection aggregation

Cong Hu, Ting Lei, Shuang Wang, Zhen Yao, Peng Wang, Tingzeng Zhang

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871D (2023) https://doi.org/10.1117/12.3005032
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

The basic idea of federated learning is to aggregate the local model parameters of all participants to obtain a global model, which is more universal and has better performance. However, existing federated learning has the problem of a unique model, where all participants train their local models based on the same global model. However, in different real-world scenarios, data often has different features and distributions, and there is no universal model suitable for learning data from multiple scenarios. Therefore, existing federated learning technologies cannot achieve optimal performance in multiple scenarios. To solve this problem, this paper proposes a decentralized federated learning model where each participant independently selects other models similar to their local models and aggregates them to obtain a specialized model applicable to the local scenario. The model aggregates the parameters based on their similarity with the local model. Models with higher similarity will have a greater weight in the aggregation process. The parameter aggregation is not performed at the central server but at each participant's local device. Through this mechanism, the proposed model achieves specialization of local models for participants and improves performance in different scenarios.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cong Hu, Ting Lei, Shuang Wang, Zhen Yao, Peng Wang, and Tingzeng Zhang "Decentralized federated learning privacy protection aggregation", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871D (16 August 2023); https://doi.org/10.1117/12.3005032

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KEYWORDS

Machine learning

Education and training

Data modeling

Performance modeling

Blockchain

Process modeling

Computer simulations

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