Decentralized federated learning privacy protection aggregation
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.
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