Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space

Federated Learning (FL) facilitates clients to collaborate on training a shared machine learning model without exposing individual private data. Nonetheless, FL remains susceptible to utility and privacy attacks, notably evasion data poisoning and model inversion attacks, compromising the system's efficiency and data privacy. Existing FL defenses are often specialized to a particular single attack, lacking generality and a comprehensive defender's perspective. To address these challenges, we introduce \textbf{F}ederated \textbf{C}ryptography \textbf{D}efense (FCD), a unified single framework aligning with the defender's perspective. FCD employs row-wise transposition cipher based data encryption with a secret key to counter both evasion black-box data poisoning and model inversion attacks. The crux of FCD lies in transferring the entire learning process into an encrypted data space and using a novel distillation loss guided by the Kullback-Leibler (KL) divergence. This measure compares the probability distributions of the local pretrained teacher model's predictions on normal data and the local student model's predictions on the same data in FCD's encrypted form. By working within this encrypted space, FCD eliminates the need for decryption at the server, resulting in reduced computational complexity. We demonstrate the practical feasibility of FCD and apply it to defend against evasion utility attack on benchmark datasets (GTSRB, KBTS, CIFAR10, and EMNIST). We further extend FCD for defending against model inversion attack in split FL on the CIFAR100 dataset. Our experiments across the diverse attack and FL settings demonstrate practical feasibility and robustness against utility evasion (impact >30) and privacy attacks (MSE >73) compared to the second best method.