What are the roles of federated learning in network data security?

Federated learning plays a significant role in improving the security of network data across distributed environments by enabling machine learning models to be trained across multiple decentralized devices or servers without exchanging raw data. This approach helps protect sensitive information by keeping it local and only sharing updates or gradients during the model training process. By decentralizing data and computation, federated learning minimizes the risk of data breaches and unauthorized access, thereby enhancing the security and privacy of network data in distributed environments.