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

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215

Proximal Policy Optimization Algorithms

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625

Reinforcement Learning: An Introduction

Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825

Untitled work

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Trust region policy optimization

Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578

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

Konda, V. R., & Tsitsiklis, J. N. (2002) · Cited by 2,202

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

Role of Federated Learning for Internet of Vehicles: A Systematic Review

Bala, P. H. M., & Chhabra, R. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-48781-1_11 · Cited by 1

A model parameter update strategy for enhanced asynchronous federated learning algorithm

Sun, Y., Gao, X., Shen, Y., Xie, J., Yang, J., & Si, N. (2023) · International Conference on Computer Systems (ICCS) · doi:10.1109/iccs59700.2023.10335537 · Cited by 1

Federated Learning for Autonomous Vehicles Control

Chen, M., & Cui, S. R. (2023) · Wireless networks · doi:10.1007/978-3-031-51266-7_6 · Cited by 1

Energy Optimization for Hierarchical Federated Learning based on Over-the-Air Computing

Zheng, K., & Li, Z. (2023) · IEEE Smart World Congress (SWC) · doi:10.1109/swc57546.2023.10448654 · Cited by 1

Towards Energy-efficient Resource Allocation for Federated Learning in Mobile Edge Computing

Ma, W., Zhao, Y., Sun, W. Y., Liu, Y., Guo, B., & Niyato, D. T. (2023) · International Conference on Artificial Intelligence of Things and Systems (AIoTSys) · doi:10.1109/aiotsys58602.2023.00060 · Cited by 1

The Performance Analysis of Federated Learning Methods for IoT with Big Data

Govindaram, A., & Jegatheesan, A. (2024) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom61295.2024.10498745 · Cited by 1

Bert-based Federated Learning to Rank Towards News Retrieval

Gan, C. G. (2023) · International Conference Big Data Engineering and Technology · doi:10.1145/3631701.3631706 · Cited by 1

HIPFL: A Hierarchical Incentive-Based Personalized Federated Learning

Liu, Y., Bao, Z., Zhang, W., Yao, S., Zhao, H., Zhang, Z., & Miao, Y. (2024) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi61353.2024.10778797 · Cited by 1

MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services

Wu, H., Zeng, H., Xiong, Z., Kang, J., Cai, Z., Chan, T.-T., Niyato, D. T., & Han, Z. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3609730 · Cited by 1

Strategic Incentivization for Locally Differentially Private Federated Learning

Pagoti, Y. K., Sinha, A., & Sural, S. (2025) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic67133.2025.00019 · Cited by 1

Federated Learning in Small-Cell Networks: Stochastic Geometry-Based Analysis on the Required Base Station Density

Nguyen, K., Nguyen, Q. A., & Hong, J.-P. (2023) · Italian National Conference on Sensors · doi:10.3390/s23167184 · Cited by 1

Efficient Client Contribution Evaluation for Horizontal Federated LearningPreprint

Zhao, J. W., Zhu, X., Wang, J., & Xiao, J. Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.13314 · Cited by 1

Reward-Based 1-bit Compressed Federated Distillation on BlockchainPreprint

Witt, L., Zafar, U., Shen, K., Sattler, F., Li, D., & Samek, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14265 · Cited by 1

Utility Fairness for the Differentially Private Federated LearningPreprint

Alvi, S. A., Hong, Y., & Durrani, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05267 · Cited by 1

Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing FrameworkPreprint

Gao, Y., Li, J., Gong, M.-G., Xie, Y., & Qin, A. K. (2021) · arXiv.org · doi:10.48550/arxiv.2104.06670

MarS-FL: A Market Share-based Decision Support Framework for Participation in Federated LearningPreprint

Wu, X., & Yu, H. (2021) · arXiv (Cornell University)

Resource allocation for federated learning enabled edge intelligence

Lim, B. W. Y. (2022) · doi:10.32657/10356/156418

An incentive mechanism for collaborative intelligence

Lin, L. Z., Zhang, Y., Xiong, J., & Lin, J. (2022) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3538641.3561497

A Novel Layered GSP Incentive Mechanism for Federated Learning Combined with Blockchain

Sun, J., Zhan, G., Liu, J., & Feng, Y. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-4775-9_38

Multi-dimensional Contract Matching Design for Federated Learning in UAV Networks

Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D. T., & Miao, C. (2022) · Wireless networks · doi:10.1007/978-3-031-07838-5_2

Secure and Privacy-Preserving Federated Learning-Based Resource Allocation for Next Generation NetworksPreprint

Dandoush, A., Gouissem, A., & Çiftler, B. S. (2023) · doi:10.36227/techrxiv.22591852.v1

Secure and Privacy-Preserving Federated Learning-Based Resource Allocation for Next Generation NetworksPreprint

Dandoush, A., Gouissem, A., & Çiftler, B. S. (2023) · doi:10.36227/techrxiv.22591852

On Dynamic Node Cooperation Strategy Design for Energy Efficiency in Hierarchical Federated Learning

Li, Z., Zou, S., & Chen, X. (2023) · Electronics · doi:10.3390/electronics12112362

Energy-Efficient Online Node Cooperation Strategy for Hierarchical Federated Learning

Zou, S., Li, Z., & Chen, X. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00045

CAIM: A Context-Aware Incentive Mechanism for Robust Federated Learning

Chang, C.-W., Cui, Y., Shen, J., & Wei, T. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00032

A Machine Learning Based Computing Node Selection Algorithm

Wei, M., Lei, B., & Huang, X. (2023) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb58369.2023.10211175

A Blockchain-Based Method for Power-Related Data Quality Auditing

Fu, M., Tao, F., Li, W., Shao, R., & Sun, Z. (2023) · Mechanisms and machine science · doi:10.1007/978-3-031-42987-3_54

Reverse Multidimensional Auction Based Vehicle Selection and Resource Allocation for IoV

Chen, J., Tang, Z., Li, Z., & Zhang, Y. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1010-2_17

Group Based Privacy Preserving Incentive Mechanism for Federated Learning

Kanchan, S., Raj, S., Ahmed, A., & Choi, B. J. (2024) · International Conference on Control, Instrumentation, Energy & Communication · doi:10.1109/ciec59440.2024.10468343

Improved BCoop, a Cooperation-Promoting Reward Scheme in Heterogeneous Federated Learning

Wu, Z., & Iyer, G. N. (2024) · International Conference on Data Engineering and Communication Systems (ICDECS) · doi:10.1109/icdecs59733.2023.10502537

Cross-Shard Blockchain-based Asynchronous Federated Learning: Stackelberg Game-based Efficient Resource Allocation

Gao, Z., Lin, Q., Lin, Y., Yang, Y., & Rui, L. (2023) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00066

FedJudge: Blockchain-based full-lifecycle trustworthy federated learning incentive mechanism

Wang, J., Zhang, R., Li, X., & Yin, H. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00066

Incentive Mechanism Based on Double Auction for Federated Learning in Satellite Edge Clouds

Xia, Q., Zhou, X., & Hou, Z. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00097

FedSC: Game-Theoretic Design of Sustainable Contracts for Unreliable Federated Edge Learning

Lu, J., Yuan, W., Jia, R., Cao, S., Wang, C., & Li, M. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3465015

Timeliness-Selective Incentive Federated Crowdsourcing

Jiang, X., Diao, H., Zhou, C., & Zhang, J. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00083

Risk prediction method of multi-source network information cross-space fusion based on reinforcement learning

Chen, Y., & Zheng, Y. (2024) · IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) · doi:10.1109/icsece61636.2024.10729351

On Quantifying the Gradient Inversion Risk of Data Reuse in Federated Learning Systems

Huang, J., Chen, L. Y., & Roos, S. (2024) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds64841.2024.00031

Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges

Cheng, H., Liu, W., Qu, Y., Fu, K., & Wang, Q. (2024) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc63484.2024.00015

High dimensional multi-attribute big data association mining method based on semantic feature fusion

Zheng, Y. (2024) · International Signal Processing, Communications and Engineering Management Conference (ISPCEM) · doi:10.1109/ispcem64498.2024.00059

GAIN: Game-Theoretic Design of Fair Incentive Mechanisms for Multiple Model Owners in Federated Learning

Hu, G., Zeng, X., Han, J., Li, B., & Lu, J. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2189-7_10

Data-Driven Incentive Mechanisms for Federated Learning in Vehicular Networks

Fathi, F., Montazeri, M., Araabi, B. N., Du, H., Niyato, D. T., & Kebriaei, H. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3547866 · Cited by 1

LeFi: Learn to Incentivize Federated Learning in Automotive Edge Computing

Zhao, M., Zhang, Y., Liu, Q., & Han, T. (2024) · Globecom · doi:10.1109/globecom52923.2024.10900959

An Incentive Mechanism for Frozen Watermark-Based Federated Learning on Electric Vehicles Suspension

Zhao, H., Shi, L., Wang, Q., Ye, X., Qi, Y., Zhang, Y., & Zhu, H. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3553906

Budget-Feasible Double Auction Mechanisms for Model Training Services in Federated Learning Market

Zhou, T., Lv, H., Liu, N., & Liu, L. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00085

An Uncertainty-Aware Multi-Dimensional Auction Mechanism Based on Age of Update for Federated Learning

Yue, H., Zheng, Z., Hong, Y., Song, Y., & Chen, Q. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033289

An Investigation on the Backdoor Framework Model of Federated Learning for Improving Network Security

Hussain, G. K. J., & Manoj, G. (2025) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla63829.2025.11040496

Robust, Unbiased, and K-Means-Based Client Contribution Evaluation

Chen, S., Ling, C., Si, Z., Yu, X., & Wang, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6462-7_28

Resource Optimization and Hyperparameter Tuning in Decentralized Federated Learning via MPC

Karami, F., & Khalaj, B. H. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11100387

FSPFL: Mitigating Communication Gap in Personalization Federated Learning Through Flexible Sparsity Allocation

Li, J., Zhou, L., Xu, K., Lin, N., Liu, S., Ye, G., Yu, Y., & Chai, H. (2025) · International Workshop on Quality of Service · doi:10.1109/iwqos65803.2025.11143282

A Game Theory Reward Model for Federated Learning with Probabilistic Verification

Auricchio, G., Clough, H. J., Ho, C., Bian, K., Dong, C., Yang, K., & Zhang, J. (2024) · doi:10.1145/3719545.3721106

Blockchain Meets Federated Learning: A Comprehensive Survey on Smart Contract-driven Optimization for Aggregation, Security, and Scalability

Boulahiat, B., Trichni, S., & Omary, F. (2025) · IEEE Congress on Information Science and Technology (CiSt) · doi:10.1109/cist65886.2025.11224214

Utility‐aware Edge Computing System for Remote Health Monitoring

Pratap, D. A., Yadav, D. R. N., Patel, D. Y. S., Yadav, D. A. K., & Ahmadian, D. A. (2025) · doi:10.1002/9781394297061.ch07

Grouping competition-based privacy allocation mechanism for federated learning

Wu, X., Zhang, L., Zhou, H., Tao, J., Gu, Y., & Shen, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114906

Semi-decentralized federated learning with client pairing for efficient mutual knowledge transfer

Yang, D., Lee, J., & Choi, S. G. (2025) · Scientific Reports · doi:10.1038/s41598-025-29491-4

Reducing pandemic outbreak risks and economic losses in aviation: A segmented incentive-penalty approach

Bo, L., Liu, Z., & Zhang, T. (2025) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2025.102947

FedPRS: A Privacy-preserving Representation Synthesis Framework for Federated Contribution Evaluation

Fan, Y., Yao, Y., Xi, W., Zhao, Q., Liu, Z., Fan, L., Yang, Q., & Jin, J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3779055

Reinforcement Learning-Based Incentive Scheme for Federated Learning in Vehicular Networks

Shan, A., Wu, C., Lin, Y., Zhong, L., Li, J., Ji, Y., & Chen, J. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3641517

Wheat Leaf Rust Recognition using Deep Learning and Federated Learning

Prajapat, V., Rathore, N. P. S., Sethi, K. K., & Rajput, S. S. (2025) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset66531.2025.11281244

Auction-based Incentive Mechanism Design for Federated Learning in Medical Image Analysis

Peng, L. (2025) · International Conference on Computer Information Science and Artificial Intelligence (CISAI) · doi:10.1145/3773365.3773586

Cost-Constrained Incentive Mechanism for Federated LearningPreprint

Wu, S., Luo, Y., Fu, S., Xu, M., & Guo, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6018076

SCFU: A Collaborative Federated Unlearning Algorithm with Weighted Penalty and Adaptive Rewards

Cai, P., Jiang, W., & Wang, B. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323166 · Cited by 1

Fair incentive allocation in vertical federated learning using nucleolus

Khan, A., ten Thij, M., Tang, G., Thuijsman, F., & Wilbik, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-343852-3.00015-2

Reputation-Driven Incentive Mechanism for Federated Learning: Mitigating Training Cost Misreporting through Evolutionary Game Theory

Shang, Y. (2025) · IEEE International Conference on Control, Electronics and Computer Technology (ICCECT) · doi:10.1109/iccect64621.2025.11339691

A Data-Balanced and Privacy-Preserving Incentive Mechanism for Federated Learning Based on MADRL

Guo, J., Sun, Z., Mu, S., Li, Z., & Liu, A. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5722-6_6

Incentive Mechanism Design in Satellite-Based Federated Learning Using Mean Field Evolutionary Approach

Kang, Y., Gao, H., & Han, Z. (2025) · Wireless networks · doi:10.1007/978-3-031-91859-9_6

Enhancing Federated Learning in IoT: A Quality‐Based Incentive Mechanism With Stackelberg Game Modelling

Li, Q., Zhao, H., Wang, Q., Zhang, W., Chen, Y., & Hu, Z. (2026) · IET Communications · doi:10.1049/cmu2.70139

FedTier: A Tiered Client Selection approach for Efficient and Fair Federated Learning

Bensiah, O. A., Benaboud, R., & Mokrani, A. (2025) · International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS) · doi:10.1109/ictaacs69003.2025.11399439

Towards Contribution-Fair Federated Learning: A Contract Theory based Incentive Mechanism under Dual Information Asymmetries

Lou, J., & Wang, H. (2025) · Asia Pacific Conference on Computing Technologies, Communications and Networking (CTCNet) · doi:10.1109/ctcnet68009.2025.11405851

FLET: Game-Theoretic Free-Riding Mitigation via Test Tasks in Federated Learning

Guo, S., Wang, Y., Su, Z., Pan, Y., Luan, T. H., & Luo, X. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2026.3668944

Optimizing Prompt Engineering for Large Language Model-Based AI Systems

Vuppala, N. S. M., Hebbar, K. S., Sharma, V., & Roy, V. (2025) · IEEE International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies (SISIMPACT) · doi:10.1109/sisimpact67725.2025.11439496

Blockchain-Based Secure and Truthful Entity Participation Scheme in Heterogeneous Federated Learning

Wang, R., Liu, X., Yang, H., Liu, D., Zhang, H., Liu, Y., & Su, Z. (2025) · International Conference on Artificial Intelligence Security and Governance (ICAISG) · doi:10.1109/icaisg68699.2025.11452138

A weighted reputation-based cooperative model to optimize internet connectivity in intermittent IoT networks

Cangondo, G. E. M., Silva, B. M. C., & Ladeira, E. F. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112278

Blockchain-Enabled Incentive Mechanism for Federated Learning: A Multi-Agent Deep Deterministic Policy Gradient Approach

Jain, V., Verma, P., Kumar, M., & Kaushik, A. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3682129

A novel approach for fair incentive social physical data based on blockchain-federated learning

Mohamed, H. G., Tran-Huy, H., Hoang-Thu, T., Qaddara, I., Choi, B. J., Alshehri, A. H., Ahad, I., & Liu, H. (2026) · Frontiers in Physics · doi:10.3389/fphy.2026.1778336

Low Energy Consumption Hierarchical Federated Learning

Zhang, S., Wen, J., Zhou, X., Xie, K., Cong, Y., Chen, X., & Liu, T. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-23447-6_25

Incentive Mechanism for Federated Learning in Data Heterogeneity and Consumer Privacy Protection

Cao, Y., Cai, H., Zhi, T., & Guan, G. (2026) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.408164

Auction-Driven Utility Maximization for Multiple Federated Learning in Smart Healthcare

Singh, M. B., & Pratap, A. (2026) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3812537

Detecting Fetal Health Conditions Using a Federated Learning Approach with an Incentive-Based Reward System

Ahmed, H., & Islam, M. M. (2025) · IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON) · doi:10.1109/spicscon69221.2025.11504117

Reinforcement Learning for Dynamic Neural Architecture Search in Federated EVCS Security

Hussain, A., Yadav, A., & Ravikumar, G. (2026) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec67884.2026.11513203

Prototype Margin-Based Client Selection for Federated Learning

Zhang, H. M., & Liu, Y. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3696246

Breaking the participation dilemma in decentralized federated learning: A multi-agent repeated game mechanism for edge networks

Zhang, M., Tang, H., (104852), Y. Z., You, W., Qiu, H., & (121778), J. Y. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112427

BlockFlow: A Scalable Blockchain Integrated Federated Learning Framework for Decentralized Aggregation and Incentive Driven Collaboration

Prity, J. A., & Mahdi, A. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11545667

Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy

Alhazmi, T. A., & Azzedin, F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-344433-3.00015-0

A Game Theory Dynamic Incentive Optimization Model for Decentralized Federated Learning

Chen, J. H., Li, D., Wang, P., Liu, P., Zhang, M., & Xian, Y. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582232

FLamma: Adaptive Gamma-Based Game-Theoretic Framework for Fair Federated Learning

Javaherian, S., Turney, B., Yuan, X., Chen, L., & Tzeng, N.-F. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3712019

UOIM: Online Incentive Design for Fair Hierarchical Federated Learning and Unlearning in Edge Computing

Qian, Y., Ma, L., Yu, G., Zheng, M., Huang, M., Li, Q., & Wang, X. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3712254

A Hierarchical Incentive Mechanism Design for Clustered Federated Reinforcement Learning With Budget Limitation: A Contract-Stackelberg Game Framework

Akbari, M., Ghasemi, S., Talabi, O., Yekanlou, A., & Cai, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3711992

Incentive Mechanism for Distributed Learning in Heterogeneous Networks: A Multi-Leader Multi-Follower Stackelberg Approach

Wang, J., Feng, G., Cheng, L., Peng, M., Qin, S., & Wei, F. (2026) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops63917.2026.11586540

RCIM-FL: Incentive Mechanism for Federated Learning Based on Reputation Mechanism and Contract Theory

Yang, X., & Yuan, W. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3450-9_7

Utility Estimation and Quality Evaluation Incentive Mechanism for Federated Learning

Tian, F., Zhang, X., & Li, Y. (2026) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2026.3716632

Mechanism Design for Federated Learning With Non-Monotonic Network Effects

Li, X., Luo, B., Huang, J., & Luo, Y. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3717853

Quantization-Aware Incentive Mechanism for Communication-Efficient Federated Learning

Cui, H., Qu, Z., Ye, B., Zhuang, T., Tang, B., & Zhou, W. (2026) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/2575-8411.2026.00064

BE-HFL: Reputation-aware Stackelberg incentive mechanism for blockchain-enabled hierarchical federated learning

Liu, D., Wang, W., Hafid, A. S., Liu, Y., & Zhao, J. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.112217

Information Scenario-Aware Incentive Mechanisms for Federated Learning Using Game Theory and Reinforcement Learning

Li, J., Wang, H., Chen, J., Hu, M., Cui, Y., & Zhou, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3723420

AI-Based Dynamic Pricing: A Cross Industry Bibliometric Review of Trends, Challenges, and Future Directions

Ozay, D., Jahanbakht, M., & Wang, S. (2026) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21080280

Trustworthy analysis of medical and biometric data with deep learning: Methods, challenges, and the imperatives of explainability and privacy

Hcini, G., & Jdey, I. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21841-2

Incentive-Driven Zero-Knowledge Proof-Based Gradient Aggregation for Federated Learning

Kowouvi, C., Ahene, E., Walker, J., Akyere, R., Agyili, D. K. O., Nani, D., & Kponyo, J. J. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-9514-3_31

Semi-Asynchronous Efficient Hierarchical Federated Learning for heterogeneous resources

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