An Efficient and Secure Self-Learning Federated Learning Method Based on Chaotic Secure Clustering for Data Processing in Internet of Things

The emergence of federated learning (FL) promotes the rapid development of artificial intelligence technology in Internet of Things (IoT). However, the huge communication cost and consumption of local computing resource in the process of FL restricts its further development and application in IoT. To solve the above problems, this paper proposes an efficient and secure self-learning federated learning method based on chaotic secure clustering. Firstly, the self-learning weights selection strategy is proposed, this strategy divides FL into two stages. In the first stage, the clients randomly select partial weights according to the inactivation parameter, which are encrypted by CKKS homomorphic encryption and uploaded, and the server updates the weights adaptive matrix according to the performance of the global model. In the second stage, the clients select some weights to encrypt and upload according to the weights adaptive matrix and inactivation parameter. Then, to further improve the efficiency of FL, we propose the chaotic secure clustering method to select some clients. The server performs clustering operation before FL. And in the clustering process, we transform and locally-globally scramble the client label information, which ensures the security of the client information in the clustering process. Finally, we use MNIST dataset, fashion-MNIST dataset, GTSRB dataset and CSE-CIC-IDS2018 dataset to simulate non-independent and identically distributed (non-IID) scenarios, and the experimental results show the effectiveness, security and efficiency of the proposed method, and our method provides a new learning paradigm for the further development and application of FL in IoT.