基于模型质量评分的联邦学习聚合算法优化.

In federated learning environments, it is crucial to assess the quality of client data, especially when a validation set is not available. Traditional evaluation methods rely on measuring the loss of client models on the validation set of a central node to assess data quality. To address these issues, this paper proposed a method for scoring model quality based on peer information. This method involved tailoring the model parameters uploaded by the client and designing a model quality scoring mechanism based on the theories of correct scoring rules. It developed an optimized aggregation algorithm, leveraging the scores of clients to mitigate the impacts of low-quality local models on the global model. Experiments conducted on datasets like MNIST, Fashion-MNIST, and CIFAR-10 demonstrate that the proposed scoring mechanism is straightforward and effective in identifying three types of low-quality data clients: free-riding clients, overly privacy-protective clients, and mislabeled clients. The proposed method enhances the robustness of federated learning performance. [ABSTRACT FROM AUTHOR]