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An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange (2312.08096v1)

Published 13 Dec 2023 in cs.LG

Abstract: Federated Learning (FL) is a distributed machine learning paradigm that addresses privacy concerns in machine learning and still guarantees high test accuracy. However, achieving the necessary accuracy by having all clients participate in FL is impractical, given the constraints of client local computing resource. In this paper, we introduce a multi-user collaborative computing framework, categorizing users into two roles: model owners (MOs) and data owner (DOs). Without resorting to monetary incentives, an MO can encourage more DOs to join in FL by allowing the DOs to offload extra local computing tasks to the MO for execution. This exchange of "data" for "computing resources" streamlines the incentives for clients to engage more effectively in FL. We formulate the interaction between MO and DOs as an optimization problem, and the objective is to effectively utilize the communication and computing resource of the MO and DOs to minimize the time to complete an FL task. The proposed problem is a mixed integer nonlinear programming (MINLP) with high computational complexity. We first decompose it into two distinct subproblems, namely the client selection problem and the resource allocation problem to segregate the integer variables from the continuous variables. Then, an effective iterative algorithm is proposed to solve problem. Simulation results demonstrate that the proposed collaborative computing framework can achieve an accuracy of more than 95\% while minimizing the overall time to complete an FL task.

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References (20)
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[2022] Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Mcmahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.y.: Communication-efficient learning of deep networks from decentralized data (2016) Li et al. [2020] Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37(3), 50–60 (2020) https://doi.org/10.1109/MSP.2020.2975749 Lim et al. [2020] Lim, W.Y.B., Luong, N.C., Hoang, D.T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., Miao, C.: Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials 22(3), 2031–2063 (2020) https://doi.org/10.1109/COMST.2020.2986024 Yang et al. [2019] Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology 10(2), 1–19 (2019) Nguyen et al. [2022] Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. Complexity 2021 (2021) Ren et al. [2019] Ren, J., Wang, H., Hou, T., Zheng, S., Tang, C.: Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access 7, 69194–69201 (2019) https://doi.org/10.1109/ACCESS.2019.2919736 Kim [2021] Kim, S.: Cooperative federated learning-based task offloading scheme for tactical edge networks. IEEE Access 9, 145739–145747 (2021) https://doi.org/10.1109/ACCESS.2021.3123313 Luo et al. [2021] Luo, Y., Xu, J., Xu, W., Wang, K.: Sliding differential evolution scheduling for federated learning in bandwidth-limited networks. IEEE Communications Letters 25(2), 503–507 (2021) https://doi.org/10.1109/LCOMM.2020.3032517 Chakraborty and Misra [2022] Chakraborty, A., Misra, S.: Qos-aware resource bargaining for federated learning over edge networks in industrial iot. IEEE Transactions on Network Science and Engineering, 1–10 (2022) https://doi.org/10.1109/TNSE.2022.3206474 Shen et al. [2022] Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37(3), 50–60 (2020) https://doi.org/10.1109/MSP.2020.2975749 Lim et al. [2020] Lim, W.Y.B., Luong, N.C., Hoang, D.T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., Miao, C.: Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials 22(3), 2031–2063 (2020) https://doi.org/10.1109/COMST.2020.2986024 Yang et al. [2019] Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology 10(2), 1–19 (2019) Nguyen et al. [2022] Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. Complexity 2021 (2021) Ren et al. [2019] Ren, J., Wang, H., Hou, T., Zheng, S., Tang, C.: Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access 7, 69194–69201 (2019) https://doi.org/10.1109/ACCESS.2019.2919736 Kim [2021] Kim, S.: Cooperative federated learning-based task offloading scheme for tactical edge networks. IEEE Access 9, 145739–145747 (2021) https://doi.org/10.1109/ACCESS.2021.3123313 Luo et al. [2021] Luo, Y., Xu, J., Xu, W., Wang, K.: Sliding differential evolution scheduling for federated learning in bandwidth-limited networks. IEEE Communications Letters 25(2), 503–507 (2021) https://doi.org/10.1109/LCOMM.2020.3032517 Chakraborty and Misra [2022] Chakraborty, A., Misra, S.: Qos-aware resource bargaining for federated learning over edge networks in industrial iot. IEEE Transactions on Network Science and Engineering, 1–10 (2022) https://doi.org/10.1109/TNSE.2022.3206474 Shen et al. [2022] Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Lim, W.Y.B., Luong, N.C., Hoang, D.T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., Miao, C.: Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials 22(3), 2031–2063 (2020) https://doi.org/10.1109/COMST.2020.2986024 Yang et al. [2019] Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology 10(2), 1–19 (2019) Nguyen et al. [2022] Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. Complexity 2021 (2021) Ren et al. [2019] Ren, J., Wang, H., Hou, T., Zheng, S., Tang, C.: Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access 7, 69194–69201 (2019) https://doi.org/10.1109/ACCESS.2019.2919736 Kim [2021] Kim, S.: Cooperative federated learning-based task offloading scheme for tactical edge networks. IEEE Access 9, 145739–145747 (2021) https://doi.org/10.1109/ACCESS.2021.3123313 Luo et al. [2021] Luo, Y., Xu, J., Xu, W., Wang, K.: Sliding differential evolution scheduling for federated learning in bandwidth-limited networks. IEEE Communications Letters 25(2), 503–507 (2021) https://doi.org/10.1109/LCOMM.2020.3032517 Chakraborty and Misra [2022] Chakraborty, A., Misra, S.: Qos-aware resource bargaining for federated learning over edge networks in industrial iot. IEEE Transactions on Network Science and Engineering, 1–10 (2022) https://doi.org/10.1109/TNSE.2022.3206474 Shen et al. [2022] Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology 10(2), 1–19 (2019) Nguyen et al. [2022] Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. Complexity 2021 (2021) Ren et al. [2019] Ren, J., Wang, H., Hou, T., Zheng, S., Tang, C.: Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access 7, 69194–69201 (2019) https://doi.org/10.1109/ACCESS.2019.2919736 Kim [2021] Kim, S.: Cooperative federated learning-based task offloading scheme for tactical edge networks. IEEE Access 9, 145739–145747 (2021) https://doi.org/10.1109/ACCESS.2021.3123313 Luo et al. [2021] Luo, Y., Xu, J., Xu, W., Wang, K.: Sliding differential evolution scheduling for federated learning in bandwidth-limited networks. IEEE Communications Letters 25(2), 503–507 (2021) https://doi.org/10.1109/LCOMM.2020.3032517 Chakraborty and Misra [2022] Chakraborty, A., Misra, S.: Qos-aware resource bargaining for federated learning over edge networks in industrial iot. IEEE Transactions on Network Science and Engineering, 1–10 (2022) https://doi.org/10.1109/TNSE.2022.3206474 Shen et al. [2022] Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. Complexity 2021 (2021) Ren et al. [2019] Ren, J., Wang, H., Hou, T., Zheng, S., Tang, C.: Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access 7, 69194–69201 (2019) https://doi.org/10.1109/ACCESS.2019.2919736 Kim [2021] Kim, S.: Cooperative federated learning-based task offloading scheme for tactical edge networks. IEEE Access 9, 145739–145747 (2021) https://doi.org/10.1109/ACCESS.2021.3123313 Luo et al. [2021] Luo, Y., Xu, J., Xu, W., Wang, K.: Sliding differential evolution scheduling for federated learning in bandwidth-limited networks. IEEE Communications Letters 25(2), 503–507 (2021) https://doi.org/10.1109/LCOMM.2020.3032517 Chakraborty and Misra [2022] Chakraborty, A., Misra, S.: Qos-aware resource bargaining for federated learning over edge networks in industrial iot. IEEE Transactions on Network Science and Engineering, 1–10 (2022) https://doi.org/10.1109/TNSE.2022.3206474 Shen et al. 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IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology 10(2), 1–19 (2019) Nguyen et al. [2022] Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. 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IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology 10(2), 1–19 (2019) Nguyen et al. [2022] Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. 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IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet of Things Journal 9(1), 359–383 (2022) https://doi.org/10.1109/JIOT.2021.3103320 Osborne [2004] Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. Complexity 2021 (2021) Ren et al. 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IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Osborne, M.J.: An introduction to game theory. New York 3 (2004) Khan et al. [2020] Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. Complexity 2021 (2021) Ren et al. [2019] Ren, J., Wang, H., Hou, T., Zheng, S., Tang, C.: Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access 7, 69194–69201 (2019) https://doi.org/10.1109/ACCESS.2019.2919736 Kim [2021] Kim, S.: Cooperative federated learning-based task offloading scheme for tactical edge networks. 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In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Khan, L.U., Pandey, S.R., Tran, N.H., Saad, W., Han, Z., Nguyen, M.N.H., Hong, C.S.: Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58(10), 88–93 (2020) https://doi.org/10.1109/MCOM.001.1900649 Xiao et al. [2020] Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. [2021] Zhou, S., Jadoon, W., Shuja, J.: Machine learning-based offloading strategy for lightweight user mobile edge computing tasks. 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[2022] Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., Zou, X.: Incentive mechanism design for federated learning: A two-stage stackelberg game approach. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pp. 148–155 (2020). https://doi.org/10.1109/ICPADS51040.2020.00029 Dong and Zhang [2020] Dong, L., Zhang, Y.: Federated learning service market: A game theoretic analysis. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 227–232 (2020). https://doi.org/10.1109/WCSP49889.2020.9299689 Jiao et al. [2021] Jiao, Y., Wang, P., Niyato, D., Lin, B., Kim, D.I.: Toward an automated auction framework for wireless federated learning services market. IEEE Transactions on Mobile Computing 20(10), 3034–3048 (2021) https://doi.org/10.1109/TMC.2020.2994639 Zhou et al. 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IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554
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IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554
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  16. Chakraborty, A., Misra, S.: Qos-aware resource bargaining for federated learning over edge networks in industrial iot. IEEE Transactions on Network Science and Engineering, 1–10 (2022) https://doi.org/10.1109/TNSE.2022.3206474 Shen et al. [2022] Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554
  17. Shen, Y., Qu, Y., Dong, C., Zhou, F., Wu, Q.: Joint training and resource allocation optimization for federated learning in uav swarm. IEEE Internet of Things Journal 10(3), 2272–2284 (2022) Wu et al. [2018] Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554
  18. Wu, D., Wang, F., Cao, X., Xu, J.: Wireless powered user cooperative computation in mobile edge computing systems. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–7 (2018). https://doi.org/10.1109/GLOCOMW.2018.8644186 Yi et al. [2022] Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554
  19. Yi, T., Zhang, G., Wang, K., Yang, K.: Joint program partitioning and resource allocation for completion time minimization in multi-mec systems. IEEE Transactions on Network Science and Engineering 9(3), 1932–1948 (2022) https://doi.org/10.1109/TNSE.2022.3155177 Yang et al. [2021] Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554 Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554
  20. Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Transactions on Wireless Communications 20(3), 1935–1949 (2021) https://doi.org/10.1109/TWC.2020.3037554
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Authors (4)
  1. Ruonan Dong (1 paper)
  2. Hui Xu (121 papers)
  3. Han Zhang (338 papers)
  4. GuoPeng Zhang (10 papers)