Round efficient privacy-preserving federated learning based on MKFHE

Abstract

Federated learning (FL) was created with the intention of enabling collaborative training of models without the need for direct data exchange. However, data leakage remains an issue in FL. Multi-Key Fully Homomorphic Encryption (MKFHE) is a promising technique that allows computations on ciphertexts encrypted by different parties. MKFHE’s aptitude to handle multi-party data makes it an ideal tool for implementing privacy-preserving federated learning.We present a multi-hop MKFHE with compact ciphertext. MKFHE allows computations on data encrypted by different parties. In MKFHE, the compact ciphertext means that the size of the ciphertext is independent of the number of parties. The multi-hop property means that parties can dynamically join the homomorphic computation at any time. Prior MKFHE schemes were limited by their inability to combine these desirable properties. To address this limitation, we propose a multi-hop MKFHE scheme with compact ciphertext based on the random sample common reference string(CRS). We construct our scheme based on the residue number system (RNS) variant CKKS17 scheme, which enables efficient homomorphic computation over complex numbers due to the RNS representations of numbers.We construct a round efficient privacy-preserving federated learning based on our multi-hop MKFHE. In FL, there is always the possibility that some clients may drop out during the computation. Previous HE-based FL methods did not address this issue. However, our approach takes advantage of multi-hop MKFHE that users can join dynamically and constructs an efficient federated learning scheme that reduces interactions between parties. Compared to other HE-based methods, our approach reduces the number of interactions during a round from 3 to 2. Furthermore, in situations where some users fail, we are able to reduce the number of interactions from 3 to just 1.