In recent years, federated learning has received much attention because it involves the collaboration of each client to train a strong model without infringing data privacy. Machinery fault diagnosis also benefits from this technology. However, the different operating conditions and diagnostic tasks for each client lead to data heterogeneity among the devices, which brings a tough challenge to federated learning for machinery fault diagnosis. To solve this problem, we explore a federated learning method for machinery fault diagnosis based on similarity collaboration (FedSC). Considering the data heterogeneity of each client, the FedSC customizes a personalized model for each client, and then a similarity mechanism is used to weigh the aggregation of each personalization model. In addition, when a client’s model is updated, the distance constraint loss is employed to ensure that local model updates do not deviate from their personalized cloud model. Comprehensive experiments on two rotating machinery datasets demonstrate that our method achieves higher accuracy and faster convergence, providing promising application prospects in realistic industrial scenarios.