Privacy-protected aggregation in federated learning based on semi-homomorphic encryption
Haifeng Lin, Chen Chen, Yunfan Hu · 2023
As people pay more and more attention to privacy protection, federated learning with privacy protection ability comes into being, and gradually becomes one of the research hotspots in the field of machine learning. As a distributed machine learning framework, federated learning can prevent data from being directly leaked, but some private information can still be derived from the gradient. Therefore, some cryptographic schemes are used to solve this privacy leakage. In this paper, Paillier algorithm in the semi-homomorphic encryption scheme is introduced to encrypt the gradient uploaded by clients in federated learning, which is aggregated by the server and sent back to each client for model update. Relevant experiments are also designed to verify the efficiency of Paillier algorithm through the change of the computing cost with the increase of the number of customers.