Double layer federated security learning architecture for artificial intelligence of things - ZHENG Chengbo; YAN Haonan; FU Caili; ZHANG Dong; LI Hui; WANG Bin (网络与信息安全学报)
Federated learning, as a distributed machine learning architecture, can complete model co-training while protecting data privacy, and is widely used in Artificial Intelligence of Things. However, there are often security threats such as privacy breaches and poisoning attacks in federated learning. In order to overcome the performance and security challenges of using federated learning for joint training among multiple institutions in the context of intelligent Internet of Things, a two-level federated security learning architecture was proposed for intelligent Internet of Things. The entire security learning system was divided into a two-level architecture of bottom and top layers. The bottom architecture consisted of various IoT devices and a server within the organization. Different devices were connected through blockchain networks, and the server detected and eliminated malicious devices through the historical gradient uploaded by the devices, avoiding slow convergence and decreased global model accuracy caused by poisoning attacks. The top-level architecture consisted of servers from different institutions, using secure multi-party computation based on secret sharing for secure aggregation, protecting gradient privacy while achieving decentralized gradient aggregation. The experimental results show that the architecture achieves detection accuracy of over 85% for four common poisoning attacks, greatly improving the security of the system and achieving decentralized security aggregation with gradient privacy protection within linear time complexity.