CCM-FL: Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing, to handle the numerous data collected by the sensors and provide the users with precise and customized services. Meanwhile, how to protect the private information of users in federated learning has become an important research topic. Compared with the differential privacy (DP) technique and secure multiparty computation (SMC) strategy, the covert communication mechanism in federated learning is more efficient and energy-saving in training the machine learning models. In this paper, we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks. Different from the previous works about covert communication in federated learning, most of which are considered in a centralized framework and experimental-based, we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents, the time complexity of which is O(log n), approximating to the optimal solution. Secondly, for the federated learning without parameter server, which is a harder case, we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log log Δ log n) times, approximating to the optimal solution. Δ is the maximum distance between any pair of learning agents. Theoretical analysis and numerical simulations are presented to show the performance of our covert communication mechanisms. We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.