DiSHE: Dispersion-Guided Selective Homomorphic Encryption for Federated Learning

Homomorphic encryption (HE) lets a federated learning server aggregate client updates that it cannot read, but encrypting every parameter is expensive. Selective HE therefore encrypts only part of each update, usually under a single mask that all clients agree on. We present DiSHE, a selective CKKS protocol in which each client chooses its own mask. Updates are cut on a public grid of ciphertext-aligned chunks, so masks that differ across clients still aggregate slot by slot. Clients rank chunks by chunk gradient dispersion (CGD), the per-coordinate sample variance of per-example gradients on a small round-start probe, and always encrypt the classifier head. The same scores define a family of aggregation rules between FedAvg and inverse-dispersion weighting. Because encryption leaves the aggregated values unchanged, the learned model depends, in exact arithmetic, only on the aggregation rule; we characterize the resulting family, including a lower bound on every client's weight, and bound the ciphertext part of the stateless broadcast. With ResNet-18, five non-IID clients, ten rounds, and a nominal encryption ratio of 10%, the final global test accuracy of DiSHE is within 0.83 points of MaskCrypt and FedML-HE on CIFAR-10 and SVHN, and within 2.30 points on Fashion-MNIST. It exceeds our clipping-and-noise versions of FAS and ParaAegis by 4.96–49.51 points. At a 10% ratio, DiSHE needs no mask exchange and has the lowest per-round protocol time (1.51 s versus 1.82-15.54 s) of the five implementations. In masked-gradient attacks on LeNet-5, DiSHE is the only method that blocks both analytic label attacks, iDLG and iLRG, in every attempt.