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Worker selection is critical to the success of federated learning, but issues such as inadequate incentives and poor-quality data can negatively impact the process. Existing studies have used the multi-weight subjective logic model, but it is vulnerable to malicious evaluation and unfair to newly added nodes. In this paper, we propose an improved reputation evaluation algorithm that allows evaluations from different sources to influence each other and reduce the impact of malicious comments. Our approach effectively distinguishes between malicious and honest users and improves worker selection and collaboration in federated learning.

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