A Survey of Security Aggregation
Sufang Zhou, Mingzhe Liao, Baojun Qiao, Xiaobo Yang · 2022 24th International Conference on Advanced Communication Technology (ICACT) · 2022
Machine learning (ML) requires the collection of large amounts of data to train robust predictive models. Federated learning (FL) allows sensitive data to be kept on the client side to train a shared model, but shared models still pose privacy concerns. Secure aggregation is an important algorithm for securing federation learning by being able to compute the sum of client-side model updates without revealing information about individual client updates. In this study, we investigate the results of secure aggregation protocols in recent years and review the research results in the field of secure aggregation according to secret sharing, differential privacy, and homomorphic encryption mechanisms. On this basis, we compare and analyze the advantages and disadvantages of different mechanisms and then evaluate the security aggregation protocols in terms of security, dynamic user robustness, computational cost, and communication cost. Finally, we provide an outlook on the future development of secure aggregation protocols and give possible future research directions.