Secure and efficient federated learning using attribute-based homomorphic encryption

This academic paper proposes a new method for federated learning (training AI models across multiple computers without sharing raw data) that uses attribute-based homomorphic encryption (a type of math that lets computers do calculations on encrypted data without decrypting it first). The approach aims to make federated learning both more secure and faster by protecting data privacy while reducing computational overhead.