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This invention relates to a method and system for defending against backdoor attacks in federated learning, belonging to the field of information security. The method first performs data preprocessing operations on the terminal before executing local training, converting the data from the time domain to the frequency domain and designing a low-pass filter to filter high-frequency signals introduced by backdoor samples. Second, malicious uploads and updates are detected on the server side, the cosine similarity between the pre-global model and the local model is calculated, and an appropriate threshold is set to filter maliciously uploaded local model updates. Finally, a global aggregation operation is performed on the remaining non-malicious uploads and updates to obtain an updated global model, and differential privacy technology is used to perturb the obtained global model. This invention fully considers the scenario where backdoor attacks occur in different locations, mitigating backdoor attacks in federated learning without affecting the model's performance on benign data, thus solving the problem of backdoor attack defense in federated learning.