Pack: Towards Communication-Efficient Homomorphic Encryption in Federated Learning

Pack: Towards Communication-Efficient Homomorphic Encryption in Federated Learning

연구 분야: Cryptography

학회: SoCC '24: Proceedings of the 2024 ACM Symposium on Cloud Computing

Federated learning allows multiple clients to collaboratively train a shared model without sharing local private data. It is regarded as privacy-preserving since only model updates are communicated. Unfortunately, it has been shown in the recent literature that, model updates transmitted by participating clients can be used by a malicious server in gradient leakage attacks to obtain private training data. To prevent such potential leakage from occurring, it has widely been acknowledged that homomorphic encryption can be used to encrypt these model updates before sending them to the server, which performs computations directly on encrypted data. Although homomorphic encryption has a strong guarantee on privacy, its practical use increases communication overhead by around 17×, even with its most efficient implementation, called CKKS. In this paper, we present Pack, a novel communication-efficient mechanism over CKKS, designed specifically to reduce the communication overhead by a substantial margin. In addition, we propose new error correction and weight filtering mechanisms in Pack to improve the accuracy of the trained model. Compared to vanilla CKKS, Pack reduces the communication overhead by 3.1×, while increasing the accuracy by 5.5% and 2.5% under the i.i.d. and non-i.i.d. settings.