Federated Unsupervised Learning

Federated Unsupervised Learning is a distributed machine learning method aimed at training models from unlabeled and decentralized data. This approach facilitates collaborative learning across multiple devices or nodes without the need to centralize data, thereby safeguarding user privacy and data security. Its objective is to enhance the model's generalization and robustness, making it suitable for large-scale, heterogeneous data environments. In practical applications, Federated Unsupervised Learning can be widely used in data preprocessing, feature extraction, and anomaly detection, among other areas, and holds significant research and application value.