Secure and Efficient Data Sharing for Indoor Positioning with Federated Learning in Mobile Blockchain Networks

Traditional indoor location data sharing methods using centralized servers face issues like safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads, hampering the growth of personalized indoor services. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) data sharing framework for indoor positioning is presented. Then, we derive training latency and reward of the individual user, and formulate latency-limited resource allocation as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demon-strate that the proposed alternating iterative algorithm achieves rapid convergence. Furthermore, when confronted with model poisoning attacks, the MBFL method exhibits superior security performance compared to the traditional FL method.