Differential Privacy Enabled Cascaded Filter for Efficient and Privacy-Preserving Federated Learning

Federated learning (FL, a way for multiple computers to train an AI model together without sharing raw data) faces a tradeoff between privacy and performance: encryption methods are slow, while differential privacy (DP, adding noise to data to hide individual information) reduces accuracy. This research proposes a cascaded filter that selectively adds noise only to the most important model parameters (the dimensions with large values and high variation) before sending them to a central server, achieving both privacy protection and better model performance than existing methods.