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

作　　者：Hongruo Zhang Yifei Zou Haofei Yin Dongxiao Yu Xiuzhen Cheng 

机构地区：[1]Institute of Intelligent Computing,School of Computer Science and Technology,Shandong University,Qingdao,266237,China

出　　处：《Digital Communications and Networks》2024年第3期597-608,共12页数字通信与网络（英文版）

基　　金：supported in part by the National Key Research and Development Program of China under Grant 2020YFB1005900;the National Natural Science Foundation of China(NSFC)under Grant 62102232,62122042,61971269;Natural Science Foundation of Shandong province under Grant ZR2021QF064.

摘　　要：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 ma-chine 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 logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical 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.

关 键 词：Covert communications Federated learning Crowd sensing SINR model 

分 类 号：TN91[电子电信—通信与信息系统] TP391.44[电子电信—信息与通信工程]