Threshold-adaptive pruning with multi-key homomorphic encryption for communication-efficient secure federated learning

Abstract

Under the federated learning framework, frequent parameter interactions between edge devices and servers result in communication inefficiency, while conventional encryption methods fail to resist multi-node collusion attacks. To address these challenges, this paper proposes an optimized federated learning scheme integrating adaptive channel pruning with multi-key homomorphic encryption. First, we construct a dynamic threshold determination mechanism that automatically calibrates channel pruning rates through precision feedback during the pre-pruning phase, achieving the optimal balance between model compression and accuracy, while significantly reducing communication bandwidth consumption compared to traditional algorithms. Second, based on the Brakerski-Gentry-Vaikuntanathan (BGV) multi-key fully homomorphic encryption architecture, we design a distributed public-key encryption protocol that enables aggregation servers to securely fuse multi-source model parameters without decryption, resisting collusion attacks from up to C−1 nodes (where C denotes the total number of devices). Experiments on MNIST and CIFAR-10 datasets demonstrate that our scheme significantly reduces communication overhead through two complementary mechanisms: adaptive pruning reduces both the computational burden of local training and the volume of parameters transmitted per round, while multi-key BGV encryption ensures privacy-preserving aggregation without decryption. This work provides a novel technical pathway for privacy-preserving federated learning in resource-constrained scenarios.