| [1] |
MCMAHAN H B , MOORE E , RAMAGE D ,et al. Communication-efficient learning of deep networks from decentralized data[J]. arXiv preprint, 2016,arXiv:1602.05629.
|
| [2] |
ZHANG J , WU Y , PAN R . Online auction-based incentive mechanism design for horizontal federated learning with budget constraint[J]. arXiv preprint, 2022,arXiv:2201.09047.
|
| [3] |
HESAMIFARD E , TAKABI H , GHASEMI M ,et al. Privacy-preserving machine learning as a service[J]. Proceedings on Privacy Enhancing Technologies, 2018,2018(3): 123-142.
|
| [4] |
TANUWIDJAJA H C , CHOI R , KIM K . A Survey on deep learning techniques for privacy-preserving[C]// Proceedings of the International Conference on Machine Learning for Cyber Security. Cham:Springer, 2019: 29-46.
|
| [5] |
MANVI S S , SHYAM G . Resource management for infrastructure as a service (IaaS) in cloud computing:a survey[J]. Journal of Network and Computer Applications, 2014(41): 424-440.
|
| [6] |
ROUGHGARDEN T . Algorithmic game theory[J]. Communications of the ACM, 2010,53(7): 78-86.
|
| [7] |
KLEMPERER P . What really matters in auction design[J]. Journal of Economic Perspectives, 2002,16(1): 169-189.
|
| [8] |
BONAWITZ K , IVANOV V , KREUTER B ,et al. Practical secure aggregation for privacy-preserving machine learning[C]// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York:ACM Press, 2017: 1175-1191.
|
| [9] |
MCMAHAN H B , MOORE E , RAMAGE D ,et al. Communication-efficient learning of deep networks from decentralized data[J]. arXiv preprint, 2016,arXiv:1602.05629.
|
| [10] |
ZHAO Y , LI M , LAI L ,et al. Federated learning with non-IID data[J]. arXiv preprint, 2018,arXiv:1806.00582.
|
| [11] |
PREUVENEERS D , RIMMER V , TSINGENOPOULOS I ,et al. Chained anomaly detection models for federated learning:an intrusion detection case study[J]. Applied Sciences, 2018,8(12): 2663.
|
| [12] |
TRAN N H , BAO W , ZOMAYA A ,et al. Federated learning over wireless networks:optimization model design and analysis[C]// Proceedings of IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2019: 1387-1395.
|
| [13] |
YANG Z H , CHEN M Z , SAAD W ,et al. Energy efficient federated learning over wireless communication networks[J]. IEEE Transactions on Wireless Communications, 2021,20(3): 1935-1949.
|
| [14] |
ZENG Q S , DU Y Q , HUANG K B ,et al. Energy-efficient radio resource allocation for federated edge learning[C]// Proceedings of 2020 IEEE International Conference on Communications Workshops (ICC Workshops). Piscataway:IEEE Press, 2020: 1-6.
|
| [15] |
LI T , SANJABI M , BEIRAMI A ,et al. Fair resource allocation in federated learning[J]. arXiv preprint, 2019,arXiv:1905.10497.
|
| [16] |
WANG X F , HAN Y W , WANG C Y ,et al. In-edge AI:intelligentizing mobile edge computing,caching and communication by federated learning[J]. IEEE Network, 2019,33(5): 156-165.
|
| [17] |
KANG J W , XIONG Z H , NIYATO D ,et al. Incentive mechanism for reliable federated learning:a joint optimization approach to combining reputation and contract theory[J]. IEEE Internet of Things Journal, 2019,6(6): 10700-10714.
|
| [18] |
ZHAN Y F , LI P , QU Z H ,et al. A learning-based incentive mechanism for federated learning[J]. IEEE Internet of Things Journal, 2020,7(7): 6360-6368.
|
| [19] |
KHAN L U , PANDEY S R , TRAN N H ,et al. Federated learning for edge networks:resource optimization and incentive mechanism[J]. IEEE Communications Magazine, 2020,58(10): 88-93.
|
| [20] |
THI LE T H , TRAN N H , TUN Y K ,et al. An incentive mechanism for federated learning in wireless cellular networks:an auction approach[J]. IEEE Transactions on Wireless Communications, 2021,20(8): 4874-4887.
|
| [21] |
ZHANG J W , WU Y Z , PAN R . Incentive mechanism for horizontal federated learning based on reputation and reverse auction[C]// Proceedings of the Web Conference 2021. New York:ACM Press, 2021: 947-956.
|
| [22] |
DING N N , FANG Z X , HUANG J W . Incentive mechanism design for federated learning with multi-dimensional private information[C]// Proceedings of 2020 18th International Symposium on Modeling and Optimization in Mobile,Ad Hoc,and Wireless Networks (WiOPT). Piscataway:IEEE Press, 2020: 1-8.
|
| [23] |
HE X Q , SHEN Y H , REN J ,et al. An online auction-based incentive mechanism for soft-deadline tasks in Collaborative Edge Computing[J]. Future Generation Computer Systems, 2022(137): 1-13.
|
| [24] |
SUN J , XU Z , YANG D ,et al. Communication-efficient vertical federated learning with limited overlapping samples[J]. arXiv preprint, 2023,arXiv:2303.16270.
|
| [25] |
HAN X , WANG L , WU J . Data valuation for vertical federated learning:an information-theoretic approach[J]. arXiv preprint, 2021,arXiv:2112.08364.
|
| [26] |
ZHANG Z X , LI X C , YANG S Y . Data pricing in vertical federated learning[C]// Proceedings of 2022 IEEE/CIC International Conference on Communications in China (ICCC). Piscataway:IEEE Press, 2022: 932-937.
|
| [27] |
WANG J , YANG D J , TANG J ,et al. Enabling ra dio-as-a-service with truthful auction mechanisms[J]. IEEE Transactions on Wireless Communications, 2017,16(4): 2340-2349.
|
