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

Abstract

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.

Keywords

Covert communications / Federated learning / Crowd sensing / SINR model

References

[1] A. Capponi, C. Fiandrino, B. Kantarci, L. Foschini, D. Kliazovich, P. Bouvry, A survey on mobile crowdsensing systems: challenges, solutions, and opportunities, IEEE Commun. Surv. Tutor. 21 (3) (2019) 2419-2465.

[2] T. Zhu, T. Shi, J. Li, Z. Cai, X. Zhou, Task scheduling in deadline-aware mobile edge computing systems, IEEE Internet Things J. 6 (3) (2019) 4854-4866.

[3] Y. Hui, Z. Su, S. Guo, Utility based data computing scheme to provide sensing service in internet of things, IEEE Trans. Emerg. Top. Comput. 7 (2) (2019) 337-348.

[4] L. Zhang, Y. Wang, K. Yan, Y. Su, N. Alharbe, S. Feng, Behaviour recognition based on the integration of multigranular motion features in the internet of things, Digit. Commun. https://www.sciencedirect.com/science/article/pii/S2352864822002206.

[5] L. Wang, K. Sun, H. Dai, W. Wang, K. Huang, A.X. Liu, X. Wang, Q. Gu, Witrace: centimeter-level passive gesture tracking using OFDM signals, IEEE Trans. Mobile Comput. 20 (4) (2021) 1730-1745.

[6] J. Zhang, Q. Yan, X. Zhu, K. Yu, Smart industrial iot empowered crowd sensing for safety monitoring in coal mine, Digit. Commun. Netw. https://doi.org/10.1016 /j.dcan.2022.08.002.

[7] X. Zhu, Y. Luo, A. Liu, W. Tang, M.Z.A. Bhuiyan, A deep learning-based mobile crowdsensing scheme by predicting vehicle mobility, IEEE Trans. Intell. Transport. Syst. 22 (2021) 4648-4659.

[8] C.H. Liu, Z. Dai, Y. Zhao, J.A. Crowcroft, D.O. Wu, K.K. Leung, Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning, IEEE Trans. Mobile Comput. 20 (2021) 130-146.

[9] Q. Zhang, Y. Wang, G. Yin, X. Tong, A.M.V.V. Sai, Z. Cai, Two-stage bilateral online priority assignment in spatio-temporal crowdsourcing, IEEE Trans. Serv. Comput. 16 (3) (2022) 2267-2282.

[10] Y. Jiang, R. Cong, C. Shu, A. Yang, Z. Zhao, G. Min, Federated Learning Based Mobile Crowd Sensing with Unreliable User Data, HPCC/SmartCity/DSS, 2020, pp. 320-327.

[11] W. Zhang, Z. Li, X. Chen, Quality-aware user recruitment based on federated learning in mobile crowd sensing, Tsinghua Sci. Technol. 26 (6) (2021) 869-877.

[12] W. Schneble, G. Thamilarasu, Attack detection using federated learning in medical cyber-physical systems, in: ICCCN, IEEE, 2019, pp. 1-8.

[13] S. Lu, Y. Zhang, Y. Wang, Decentralized federated learning for electronic health records, in: 54th Annual Conference on Information Sciences and Systems (CISS), IEEE, 2020, pp. 1-5.

[14] L. Wang, W. Li, K. Sun, F. Zhang, T. Gu, C. Xu, D. Zhang, Loear: push the range limit of acoustic sensing for vital sign monitoring, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6 (3) (2022) 145:1-145:24.

[15] Y. Wang, Z. Su, N. Zhang, A. Benslimane, Learning in the air: secure federated learning for uav-assisted crowdsensing, IEEE Trans. Netw. Sci. Eng. 8 (2021) 1055-1069.

[16] B. Zhao, X. Liu, W.-N. Chen, When crowdsensing meets federated learning: privacy-preserving mobile crowdsensing system, ArXiv abs/2102.10109.

[17] V. Mothukuri, R.M. Parizi, S. Pouriyeh, Y. ping Huang, A. Dehghantanha, G. Srivastava, A survey on security and privacy of federated learning, Future Generat. Comput. Syst. 115 (2021) 619-640.

[18] M. Hao, H. Li, G. Xu, S. Liu, H. Yang, Towards efficient and privacy-preserving federated deep learning, in: IEEE International Conference on Communications, IEEE, 2019, pp. 1-6.

[19] Z. Cai, X. Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Trans. Netw. Sci. Eng. 7 (2) (2020) 766-775.

[20] W. Wang, Y. Wang, P. Duan, T. Liu, X. Tong, Z. Cai, A triple real-time trajectory privacy protection mechanism based on edge computing and blockchain in mobile crowdsourcing, IEEE Trans. Mobile Comput. (2022) 1-18.

[21] M. Seif, R. Tandon, M. Li, Wireless federated learning with local differential privacy, in: IEEE International Symposium on Information Theory, 2020, pp. 2604-2609.

[22] S. Chen, D. Yu, Y. Zou, J. Yu, X. Cheng, Decentralized wireless federated learning with differential privacy, IEEE Trans. Ind. Inf. 18 (9) (2022) 6273-6282.

[23] L. Xie, K. Lin, S. Wang, F. Wang,J. Zhou, Differentially private generative adversarial network, ArXiv abs/1802.06739.

[24] R. Canetti, U. Feige, O. Goldreich, M. Naor, Adaptively secure multi-party computation, in: G.L. Miller (Ed.), ACM Symposium on the Theory of Computing, 1996, pp. 639-648.

[25] L.T. Phong, Y. Aono, T. Hayashi, L. Wang, S. Moriai, Privacy-preserving deep learning via additively homomorphic encryption, IEEE Trans. Inf. Forensics Secur. 13 (5) (2018) 1333-1345.

[26] M. Hao, H. Li, X. Luo, G. Xu, H. Yang, S. Liu, Efficient and privacy-enhanced federated learning for industrial artificial intelligence, IEEE Trans. Ind. Inf. 16 (10)(2020) 6532-6542.

[27] I. Siniosoglou, V. Argyriou, T. Lagkas, A. Tsiakalos, A. Sarigiannidis, P. G. Sarigiannidis, Covert distributed training of deep federated industrial honeypots, in: IEEE Globecom, pp. 1-6.

[28] Y. Xie, J. Kang, D. Niyato, N. T. T. Van, N. C. Luong, Z. Liu, H. Yu, Securing federated learning: a covert communication-based approach, CoRR abs/ 2110.02221.

[29] W. Degang, S. Yi, Z. Chuanxin, A covert communication method based on gradient model, ICSIP (2021) 926-930.

[30] N.T.T. Van, N.C. Luong, H.T. Nguyen, S. Feng, D. Niyato, D.I. Kim, Latency minimization in covert communication-enabled federated learning network, IEEE Trans. Veh. Technol. 70 (12) (2021) 13447-13452.

[31] Y. Shi, Y. E. Sagduyu, Jamming attacks on federated learning in wireless networks, CoRR abs/2201.05172.

[32] C.H. Liu, Z. Chen, Y. Zhan, Energy-efficient distributed mobile crowd sensing: a deep learning approach, IEEE J. Sel. Area. Commun. 37 (6) (2019) 1262-1276.

[33] W. Zhang, Z. Li, X. Chen, Quality-aware user recruitment based on federated learning in mobile crowd sensing, Tsinghua Sci. Technol. 26 (6) (2021) 869-877.

[34] R. Bi, M. Zhao, Z. Ying, Y. Tian, J. Xiong, Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of iot, Digit. Commun. Netw (2020). https://doi.org/10.1016/j.dcan.2022.07.013.

[35] Y. Wang, Z. Su, N. Zhang, A. Benslimane, Learning in the air: secure federated learning for uav-assisted crowdsensing, IEEE Trans. Netw. Sci. Eng. 8 (2) (2021) 1055-1069.

[36] C. Ying, H. Jin, X. Wang, Y. Luo, Double insurance: incentivized federated learning with differential privacy in mobile crowdsensing, 2020 International Symposium on Reliable Distributed Systems (SRDS), IEEE, pp. 81-90.

[37] W. Wang, Y. Wang, Y. Huang, C. Mu, Z. Sun, X. Tong, Z. Cai, Privacy protection federated learning system based on blockchain and edge computing in mobile crowdsourcing, Comput. Network. 215 (2022) 109206.

[38] C. Dwork, A. Roth, The algorithmic foundations of differential privacy, Found. Trends® Theor. Comput. Sci. 9 (3-4) (2014) 211-407.

[39] D.C. Nguyen, M. Ding, Q.-V. Pham, P.N. Pathirana, L.B. Le, A. Seneviratne, J. Li, D. Niyato, H.V. Poor, Federated learning meets blockchain in edge computing: opportunities and challenges, IEEE Internet Things J. 8 (16) (2021) 12806-12825.

[40] F. Bayatbabolghani, M. Blanton, Secure multi-party computation, the 2018 ACM SIGSAC Conference, ACM, pp. 2157-2159.

[41] L.T. Phong, Y. Aono, T. Hayashi, L. Wang, S. Moriai, Privacy-preserving deep learning via additively homomorphic encryption, IEEE Trans. Inf. Forensics Secur. 13 (2018) 1333-1345.

[42] S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, A hybrid approach to privacy-preserving federated learning, Informatik Spektrum 42 (2018) 356-357.

[43] H. Zhang, Y. Zou, D. Yu, J. Yu, X. Cheng, Covert communications with friendly jamming in internet of vehicles, Veh. Commun. 35 (2022) 100472.

[44] O. Goussevskaia, R. Wattenhofer, M.M. Halldórsson, E. Welzl, Capacity of arbitrary wireless networks, in: INFOCOM, IEEE, 2009, pp. 1872-1880.

[45] M.M. Halldórsson, R. Wattenhofer, Wireless communication is in APX, International Colloquium on Automata, Languages, and Programming(ICALP), Springer, 2009, pp. 525-536.

[46] O. Goussevskaia, Y.A. Oswald, R. Wattenhofer, in: Complexity in Geometric SINR, Mobile Ad Hoc Networking and Computing, ACM, 2007, pp. 100-109.

[47] M.M. Halldórsson, S. Holzer, P. Mitra, R. Wattenhofer, The Power of Non-uniform Wireless Power, Proceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete algorithms, in: ACM, 2013, pp. 1595-1606.

[48] T. Kesselheim, B. Vöcking, Distributed contention resolution in wireless networks, in: International Symposium on Distributed Computing, Springer, 2010, pp. 163-178.

[49] G.S. Brar, D.M. Blough, P. Santi, Computationally efficient scheduling with the physical interference model for throughput improvement in wireless mesh networks, in: MOBICOM, ACM, 2006, pp. 2-13.