Why FHE in Federated Learning?
1. Enhanced Privacy:
FL already ensures data stays on the client side, but transmitting gradients or model updates to a central server can still leak sensitive information through inference attacks.
FHE adds an extra layer of security by ensuring that even the server cannot access the raw gradients or updates—it only processes encrypted data.
Homomorphic encryption is a cutting-edge cryptographic technique that allows computations on encrypted data without decryption, ensuring the underlying information’s confidentiality.
This encryption method is classified into three main categories: Partial Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE).
Fully Homomorphic Encryption (FHE) What is Fully Homomorphic Encryption? Fully Homomorphic Encryption (FHE) is an innovative cryptographic technique that enables computations to be performed directly on encrypted data without needing to decrypt it first. This feature ensures that sensitive data remains secure throughout the computational process, significantly reducing the risk of unauthorized access. Unlike traditional…
