FreeFL: Privacy-Preserving Cross-Silo Federated Learning Without Third Party

Cross-silo federated learning (FL, a method where organizations train AI models together by sharing only local gradients instead of raw data) has privacy risks because gradients can leak sensitive information. FreeFL is a new approach that eliminates the need for a trusted third party and a centralized aggregator by using decentralized symmetric encryption with additive homomorphism (a type of encryption that allows computation on encrypted data), achieving better efficiency in both computation and communication than existing methods.