Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning

TL;DR

This paper introduces a practical, efficient encryption protocol for federated deep learning that eliminates the need for a trusted third party, reduces computational costs, and maintains security through collaborative key generation and quantization.

Contribution

It proposes a novel encryption scheme with collaborative key generation, parameter quantization, and threshold secret sharing, improving efficiency and security in federated deep learning.

Findings

Reduces communication costs significantly.

Decreases computational complexity compared to existing methods.

Maintains model performance and security in federated learning.

Abstract

Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuited for federated learning and vulnerable to security risks. Moreover, encrypting all model parameters is computationally intensive, especially for large machine learning models such as deep neural networks. In order to mitigate these issues, we develop a practical, computationally efficient encryption based protocol for federated deep learning, where the key pairs are collaboratively generated without the help of a third party. By quantization of the model parameters on the clients and an approximated aggregation on the server, the proposed method avoids encryption and decryption of the entire model. In addition, a threshold based secret sharing technique is designed so that no one can hold the global private key for decryption, while aggregated ciphertexts can be successfully decrypted by a threshold number of clients even if some clients are offline. Our experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.