  Federated learning is a model for privacy without revealing private data by
transfer models instead of personal and private data from local client devices.
While, in the global model, it's crucial to recognize each local data is
normal. This paper suggests one method to separate normal locals and abnormal
locals by Euclidean similarity clustering of vectors extracted by inputting
dummy data in local models. In a federated classification model, this method
divided locals into normal and abnormal.
