A Learning-Based Incentive Mechanism for Federated Learning

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Reinforcement Learning: An Introduction

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Communication-Efficient Learning of Deep Networks from Decentralized Data

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Proximal Policy Optimization Algorithms

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Reinforcement Learning: An Introduction

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Trust region policy optimization

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Real-time status: How often should one update?

Kaul, S. K., Yates, R. D., & Gruteser, M. (2012) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2012.6195689 · Cited by 2,508

Actor-critic algorithms

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Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566

A Blockchain-Enabled Explainable Federated Learning for Securing Internet-of-Things-Based Social Media 3.0 Networks

Salim, S., Turnbull, B., & Moustafa, N. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3134463 · Cited by 55

A Distributed Incentive Mechanism to Balance Demand and Communication Overhead for Multiple Federated Learning Tasks in IoV

Fu, Y., Dong, M., Zhou, L., Li, C., Yu, F. R., & Cheng, N. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3510561 · Cited by 56

Federated learning based on Stackelberg game in unmanned-aerial-vehicle-enabled mobile edge computing

Li, C., Song, M., & Luo, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121023 · Cited by 50

Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC

Huang, J., Ma, B., Wang, M., Zhou, X., Yao, L., Wang, S., Qi, L., & Chen, Y. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3342187 · Cited by 50

Federal learning edge network based sentiment analysis combating global COVID-19

Liang, W., Chen, X., Huang, S., Xiong, G., Yan, K., & Zhou, X. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.03.009 · Cited by 49

Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey

Asad, M., Shaukat, S., Hu, D., Wang, Z., Javanmardi, E., Nakazato, J., & Tsukada, M. (2023) · Sensors · doi:10.3390/s23177358 · Cited by 62

Federated learning: a comprehensive review of recent advances and applications

Kaur, H., Rani, V., Kumar, M., Sachdeva, M., Mittal, A. K., & Kumar, K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17737-0 · Cited by 54

A Comprehensive Survey on Training Acceleration for Large Machine Learning Models in IoT

Wang, H., Qu, Z., Zhou, Q., Zhang, H., Luo, B., Xu, W., Guo, S., & Li, R. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3111624 · Cited by 54

Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing

Zheng, J., Li, K., Tovar, E., & Guizani, M. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498853 · Cited by 48

Efficient Client Contribution Evaluation for Horizontal Federated Learning

Zhao, J., Zhu, X., Wang, J., & Xiao, J. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413377 · Cited by 50

Applying Federated Learning in Software-Defined Networks: A Survey

Ma, X., Liao, L., Li, Z., Lai, R. X., & Zhang, M. (2022) · Symmetry · doi:10.3390/sym14020195 · Cited by 50

Communication-Efficient Federated Learning

Kishor, K. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-85559-8_9 · Cited by 49

A systematic review of federated learning from clients’ perspective: challenges and solutions

Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10563-8 · Cited by 55

Incentive-Aware Autonomous Client Participation in Federated Learning

Hu, M., Wu, D., Zhou, Y., Chen, X., & Chen, M. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3148113 · Cited by 49

Reputation-enabled Federated Learning Model Aggregation in Mobile Platforms

Wang, Y., & Kantarcı, B. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500928 · Cited by 48

Blockchain-Based Privacy-Preserving Medical Data Sharing Scheme Using Federated Learning

Zhang, H., Li, G., Zhang, Y., Gai, K., & Meikang Qiu, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-82153-1_52 · Cited by 43

A blockchain-based audit approach for encrypted data in federated learning

Sun, Z., Wan, J., Yin, L., Cao, Z., Luo, T., & Wang, B. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.05.006 · Cited by 45

Multifactor Incentive Mechanism for Federated Learning in IoT: A Stackelberg Game Approach

Chen, Y., Hui, Z., Li, T., Li, J., & Zhou, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3306778 · Cited by 45

Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis

Huang, G., Wu, Q., Sun, P., Ma, Q., & Chen, X. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3354713 · Cited by 43

Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks

Cheng, Z., Liwang, M., Xia, X., Min, M., Wang, X., & Du, X. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3184026 · Cited by 41

A Survey on Blockchain-Based Federated Learning

Wu, L., Ruan, W., Hu, J., & He, Y. (2023) · Future Internet · doi:10.3390/fi15120400 · Cited by 48

Artificial Intelligence for Web 3.0: A Comprehensive Survey

Shen, M., Tan, Z., Niyato, D. T., Liu, Y., Kang, J., Xiong, Z., Zhu, L., Wang, W., & Shen, X. (2024) · ACM Computing Surveys · doi:10.1145/3657284 · Cited by 49

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

Kegyes, T., Süle, Z., & Abonyi, J. (2021) · Complexity · doi:10.1155/2021/7179374 · Cited by 40

A Survey on Soft Computing Techniques for Federated Learning- Applications, Challenges and Future Directions

Supriya, Y., & Gadekallu, T. R. (2023) · Journal of Data and Information Quality · doi:10.1145/3575810 · Cited by 42

Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC

Xu, Z., Li, D., Liang, W., Xu, W., Xia, Q., Zhou, P., RANA, O. F., & Li, H. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3262829 · Cited by 44

FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit

Wu, C., Zhu, Y., Zhang, R., Chen, Y., Wang, F., & Cui, S. R. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3264677 · Cited by 40

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Kim, S. (2020) · IEEE Access · doi:10.1109/access.2020.3030888 · Cited by 40

A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods

Khajehali, N., Yan, J. H., Chow, Y.-W., & Fahmideh, M. (2023) · Sensors · doi:10.3390/s23167235 · Cited by 41

Ten Challenging Problems in Federated Foundation Models

Fan, T., Gu, H., Cao, X., Chan, C. S., Chen, Q., Chen, Y., Feng, Y., Gu, Y., Geng, J., Luo, B., et al. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3555328 · Cited by 49

Incentive Mechanism for Differentially Private Federated Learning in Industrial Internet of Things

Xu, Y., Xiao, M., Tan, H., Liu, A., Gao, G., & Yan, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3134257 · Cited by 39

Distributed resource scheduling in edge computing: Problems, solutions, and opportunities

Sahni, Y., Cao, J., Yang, L., & Wang, S. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109430 · Cited by 38

Tiny Multiagent DRL for Twins Migration in UAV Metaverses: A Multileader Multifollower Stackelberg Game Approach

Kang, J., Zhong, Y., Xu, M., Nie, J., Wen, J., Du, H., Ye, D., Huang, X., Niyato, D. T., & Xie, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3360183 · Cited by 38

Blockchain-Empowered Federated Learning Approach for an Intelligent and Reliable D2D Caching Scheme

Cheng, R., Sun, Y., Liu, Y., Xia, L., Feng, D., & Imran, M. A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3103107 · Cited by 38

Edge Learning

Zhang, J., Qu, Z., Chen, C., Wang, H., Zhan, Y., Ye, B., & Guo, S. (2021) · ACM Computing Surveys · doi:10.1145/3464419 · Cited by 46

PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning

Wang, D., Ren, J., Wang, Z., Wang, Y., & Zhang, Y. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3230904 · Cited by 36

Tensor-Empowered Federated Learning for Cyber-Physical-Social Computing and Communication Systems

Yang, L. T., Zhao, R., Liu, D., Lu, W., & Deng, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3282264 · Cited by 36

A Many-objective Joint Device Selection and Aggregation Scheme for Federated Learning in IoV

Wen, J., Cui, Z., Zhang, H., & Chen, J. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3701035 · Cited by 36

FL-Market: Trading Private Models in Federated Learning

Zheng, S., Cao, Y., Yoshikawa, M., Li, H., & Qiang, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020232 · Cited by 34

Distributed Orchestration of Service Function Chains for Edge Intelligence in the Industrial Internet of Things

Chen, H., Wang, S., Li, G., Nie, L., Wang, X., & Ning, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3131757 · Cited by 34

Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach

Zhang, C., Zhu, Y., Markos, C., Yu, S., & Yu, J. J. Q. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3132056 · Cited by 34

A Novel Joint Dataset and Computation Management Scheme for Energy-Efficient Federated Learning in Mobile Edge Computing

Kim, J., Kim, D., Lee, J., & Hwang, J. (2022) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2022.3147236 · Cited by 35

A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

Zhu, Y., Liu, Z., Wang, P., & Du, C. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.04.005 · Cited by 34

Distributed Intelligence in Wireless Networks

Liu, X., Yu, J., Liu, Y., Gao, Y., Mahmoodi, T., Lambotharan, S., & Tsang, D. H. K. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3265425 · Cited by 41

Traffic Sign Recognition Using Optimized Federated Learning in Internet of Vehicles

Lian, Z., Zeng, Q., Wang, W., Xu, D., Meng, W., & Su, C. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3312348 · Cited by 33

Fed-ESD: Federated learning for efficient epileptic seizure detection in the fog-assisted internet of medical things

Ding, W., Abdel‐Basset, M., Hawash, H., Abdel-Razek, S., & Liu, C.-S. (2023) · Information Sciences · doi:10.1016/j.ins.2023.02.052 · Cited by 33

Incentive Mechanism Design for Resource Sharing in Collaborative Edge LearningPreprint

Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D. T., Leung, C., Miao, C., & Yang, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.00511 · Cited by 36

TruFLaaS: Trustworthy Federated Learning as a Service

Mazzocca, C., Romandini, N., Mendula, M., Montanari, R., & Bellavista, P. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3282899 · Cited by 35

Federated learning enables privacy-preserving and data-efficient dimension prediction and part qualification across additive manufacturing factories

Mehta, M., Bimrose, M. V., McGregor, D. J., King, W. P., & Shao, C. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.04.031 · Cited by 36

Estimation of Individual Device Contributions for Incentivizing Federated Learning

Nishio, T., Shinkuma, R., & Mandayam, N. B. (2020) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps50303.2020.9367484 · Cited by 34

Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks

Zarandi, S., & Tabassum, H. (2021) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops50388.2021.9473821 · Cited by 32

Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective

Huang, G., Chen, X., Ouyang, T., Ma, Q., Chen, L., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3194198 · Cited by 35

Semi-Supervised Federated Learning Over Heterogeneous Wireless IoT Edge Networks: Framework and Algorithms

Albaseer, A. M., Abdallah, M. M., Al‐Fuqaha, A., Erbad, A., & Dobre, O. A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3194833 · Cited by 32

FS-REAL: Towards Real-World Cross-Device Federated Learning

Chen, D., Gao, D., Xie, Y., Pan, X., Li, Z., Li, Y., Ding, B., & Zhou, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599829 · Cited by 42

Blockchain-Empowered Federated Learning: Benefits, Challenges, and Solutions

Cai, Z., Chen, J., Fan, Y., Zheng, Z., & Li, K. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3541560 · Cited by 40

Incentivizing Federated Learning Under Long-Term Energy Constraint via Online Randomized Auctions

Yuan, Y., Jiao, L., Zhu, K., & Zhang, L. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3137024 · Cited by 31

Digital Twins Enabled On-Demand Matching for Multi-Task Federated Learning in HetVNets

Hui, Y., Zhao, G., Li, C., Cheng, N., Yin, Z., Luan, T. H., & Xiao, X. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3211005 · Cited by 31

Coordinate-based efficient indexing mechanism for intelligent IoT systems in heterogeneous edge computing

Tang, S., Du, X., Lu, Z., Gai, K., Wu, J., Hung, P. C. K., & Choo, K. R. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.04.012 · Cited by 30

A systematic review of federated learning incentive mechanisms and associated security challenges

Ali, A., Ilahi, I., Qayyum, A., Mohammed, I., Al‐Fuqaha, A., & Qadir, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100593 · Cited by 34

FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning

Yan, B., Liu, B., Wang, L., Zhou, Y., Liang, Z., Liu, M., & Xu, C. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534451 · Cited by 31

PASS: A Parameter Audit-Based Secure and Fair Federated Learning Scheme Against Free-Rider Attack

Wang, J., Chang, X., Mišić, J., Mišić, V. B., & Wang, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288936 · Cited by 34

Federated Learning for Urban Sensing Systems: A Comprehensive Survey on Attacks, Defences, Incentive Mechanisms, and Applications

Kapoor, A., & Kumar, D. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3434510 · Cited by 32

Incentive Mechanism Design for Federated Learning: A Two-stage Stackelberg Game Approach

Xiao, G., Xiao, M., Gao, G., Zhang, S., Zhao, H., & Zou, X. J. (2020) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads51040.2020.00029 · Cited by 29

Energy-Efficient and Privacy-Preserved Incentive Mechanism for Mobile Edge Computing-Assisted Federated Learning in Healthcare System

Liu, J., Chang, Z., Wang, K., Zhao, Z., & Hämäläinen, T. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3414417 · Cited by 29

URLLC resource slicing and scheduling for trustworthy 6G vehicular services: A federated reinforcement learning approach

Hao, M., Ye, D., Wang, S., Tan, B., & Yu, R. (2021) · Physical Communication · doi:10.1016/j.phycom.2021.101470 · Cited by 28

CoPiFL: A collusion-resistant and privacy-preserving federated learning crowdsourcing scheme using blockchain and homomorphic encryption

Xiong, R., Ren, W., Zhao, S., He, J., Ren, Y. C., Choo, K. R., & Min, G. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.016 · Cited by 28

Blockchain-based fine-grained digital twin sharing framework for social manufacturing

Li, M., Li, M., Harish, A. R., & Huang, G. Q. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102225 · Cited by 28

A Hierarchical Incentive Mechanism for Federated Learning

Huang, J., Ma, B., Wu, Y., Chen, Y., & Shen, X. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3423399 · Cited by 27

Federated Reinforcement Learning for Wireless Networks: Fundamentals, Challenges and Future Research Trends

Das, S. K., Mudi, R., Rahman, M. S., Rabie, K. M., & Li, X. (2024) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2024.3466858 · Cited by 27

Multitask Learning Assisted Driver Identity Authentication and Driving Behavior Evaluation

Xun, Y., Liu, J., & Shi, Z. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3034276 · Cited by 28

A Dynamic Contribution Measurement and Incentive Mechanism for Energy-Efficient Federated Learning in 6G

Wang, P., Ma, W., Zhang, H., Sun, W. Y., & Xu, L. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9882278 · Cited by 27

ModularFed: Leveraging modularity in federated learning frameworks

Arafeh, M., Otrok, H., Ould‐Slimane, H., Mourad, A., Talhi, C., & Damiani, E. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100694 · Cited by 29

CFSL: A Credible Federated Self-Learning Framework

Zhang, W., Bao, Z., Liu, Y., Xu, L. J., Lu, Q., Ning, H., Wang, X., Yang, S., Wang, F., & Li, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3286398 · Cited by 27

Optimizing AIGC Services by Prompt Engineering and Edge Computing: A Generative Diffusion Model-Based Contract Theory Approach

Ye, D., Cai, S., Du, H., Kang, J., Liu, Y., Yu, R., & Niyato, D. T. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3463420 · Cited by 29

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Tay, S. S., Xu, X., Foo, C.-S., & Low, B. K. H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21177 · Cited by 26

A Stackelberg Incentive Mechanism for Wireless Federated Learning With Differential Privacy

Yi, Z., Jiao, Y., Dai, W., Li, G., Wang, H., & Xu, Y. (2022) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2022.3181509 · Cited by 26

A Joint Communication and Computation Framework for Digital Twin Over Wireless Networks

Yang, Z., Chen, M., Liu, Y., & Zhang, Z. (2023) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2023.3347931 · Cited by 26

A Systematic Literature Review on Federated Learning: From A Model Quality Perspective

Liu, Y., Zhang, L., Ge, N., & Li, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01973 · Cited by 45

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

Mishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Cluster Computing · doi:10.1007/s10586-022-03813-x · Cited by 25

A Game-Theoretic Federated Learning Framework for Data Quality Improvement

Zhang, L., Zhu, T., Xiong, P., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3230959 · Cited by 25

Affordable federated edge learning framework via efficient Shapley value estimation

Dong, L., Liu, Z., Zhang, K., Yassine, A., & Hossain, M. S. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.05.007 · Cited by 25

Incentive Mechanism for Federated Learning With Random Client Selection

Wu, H., Tang, X., Zhang, Y. A., & Gao, L. X. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3334476 · Cited by 24

Participant and Sample Selection for Efficient Online Federated Learning in UAV Swarms

Wu, F., Qu, Y., Wu, T., Dong, C., Guo, K., Wu, Q., & Guo, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3363181 · Cited by 24

Bayesian Game-Driven Incentive Mechanism for Blockchain-Enabled Secure Federated Learning in 6G Wireless Networks

Cai, L., Dai, Y., Hu, Q., Zhou, J., Zhang, Y., & Jiang, T. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3405070 · Cited by 24

Quality-Aware Incentive Mechanism Design Based on Matching Game for Hierarchical Federated Learning

Du, H., Li, Z., & Chen, X. (2022) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798096 · Cited by 24

Joint Server Selection and Handover Design for Satellite-Based Federated Learning Using Mean-Field Evolutionary Approach

Kang, Y., Zhu, Y., Wang, D., Han, Z., & Başar, T. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3328776 · Cited by 24

FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing

Li, P., Huang, X., Pan, M., & Yu, R. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685582 · Cited by 23

Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning

Tang, X., & Yu, H. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/474 · Cited by 23

Economic and Energy-Efficient Wireless Federated Learning Based on Stackelberg Game

Zhao, H., Zhou, M., Xia, W., Ni, Y., Gui, G., & Zhu, H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3319160 · Cited by 23

Multi-Agent Deep Reinforcement Learning Based Incentive Mechanism for Multi-Task Federated Edge Learning

Zhao, N., Pei, Y., Liang, Y., & Niyato, D. T. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3276898 · Cited by 23

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

Khani, M., Jamali, S., Sohrabi, M. K., Sadr, M. M., & Ghaffari, A. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4929 · Cited by 22

FedDCS: A distributed client selection framework for cross device federated learning

Panigrahi, M., Bharti, S., & Sharma, A. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.001 · Cited by 22

Snowball: Energy Efficient and Accurate Federated Learning With Coarse-to-Fine Compression Over Heterogeneous Wireless Edge Devices

Li, P., Cheng, G., Huang, X., Kang, J., Yu, R., Wu, Y., Pan, M., & Niyato, D. T. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3245601 · Cited by 22

Joint Edge Server Selection and Data Set Management for Federated-Learning-Enabled Mobile Traffic Prediction

Kim, D., Shin, S., Jeong, J., & Lee, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3301019 · Cited by 22

Online Incentive Mechanism Designs for Asynchronous Federated Learning in Edge Computing

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