A Crowdsourcing Framework for On-Device Federated Learning

Convex Optimization

Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075

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

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,917

Federated Optimization in Heterogeneous Networks

Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023

Federated Learning: Strategies for Improving Communication Efficiency

Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766

Mobile crowdsensing: current state and future challenges

Ganti, R., Ye, F., & Lei, H. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.6069707 · Cited by 2,075

Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563

Elementary Numerical Analysis: An Algorithmic Approach

W., J. W., & Conte, S. D. (1967) · Mathematics of Computation · doi:10.2307/2003277 · Cited by 1,335

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Lin, Y., Han, S., Mao, H., Wang, Y., & Dally, W. J. (2017) · arXiv · doi:10.48550/arxiv.1712.01887 · Cited by 1,134

A survey on federated learning

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899

A review of applications in federated learning

Li, L., Fan, Y., Tse, M., & Lin, K.-Y. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106854 · Cited by 1,559

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3013541 · Cited by 733

Federated learning meets blockchain in edge computing: Opportunities and challenges

Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661

Federated learning review: Fundamentals, enabling technologies, and future applications

Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

Khan, L. U., Pandey, S. R., Tran, N. H., Saad, W., Han, Z., Nguyen, M. N. H., & Hong, C. S. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900649 · Cited by 506

Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems

Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3058573 · Cited by 506

Federated learning over wireless networks Convergence analysis and resource allocation

Dinh, C. T., Tran, N. H., Nguyen, M. N. H., Hong, C. S., Bao, W., Zomaya, A. Y., & Gramoli, V. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2020.3035770 · Cited by 483

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Khan, L. U., Yaqoob, I., Tran, N. H. K., Kazmi, S. M. A., Dang, T. N., & Hong, C. S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2987070 · Cited by 530

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Du, Z., Wu, C., Yoshinaga, T., Yau, K.-L. A., Ji, Y., & Li, J. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2992630 · Cited by 393

Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach

Ye, D., Yu, R., Pan, M., & Han, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2968399 · Cited by 381

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

Sun, W., Lei, S.-Y., Wang, L., Liu, Z., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3034674 · Cited by 279

Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities

Khan, L. U., Han, Z., Saad, W., Hossain, E., Guizani, M., & Hong, C. S. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3198273 · Cited by 340

Secure and Efficient Federated Learning for Smart Grid With Edge-Cloud Collaboration

Su, Z., Wang, Y., Luan, T. H., Zhang, N., Li, F., Чен, Т., & Cao, H. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3095506 · Cited by 311

Optimizing Federated Learning in Distributed Industrial IoT: A Multi-Agent Approach

Zhang, W., Yang, D., Wu, W. R., Peng, H., Zhang, N., Zhang, H., & Shen, X. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118352 · Cited by 311

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Rahman, M. A., Hossain, M. S., Islam, M. S., Alrajeh, N. A., & Muhammad, G. (2020) · IEEE Access · doi:10.1109/access.2020.3037474 · Cited by 312

A Survey of Incentive Mechanism Design for Federated Learning

Zhan, Y., Zhang, J., Hong, Z., Wu, L., Li, P., & Guo, S. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3063517 · Cited by 301

Edge Intelligence: Empowering Intelligence to the Edge of Network

Xu, D., Li, T., Li, Y., Su, X. J., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3119950 · Cited by 296

FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation

Deng, Y., Lyu, F., Ren, J., Chen, Y., Yang, P., Zhou, Y., & Zhang, Y. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488743 · Cited by 233

Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions

Kumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105894 · Cited by 259

An Incentive Mechanism for Federated Learning in Wireless Cellular Networks: An Auction Approach

Le, T. H. T., Tran, N. H., Tun, Y. K., Nguyen, M. N. H., Pandey, S. R., Han, Z., & Hong, C. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3062708 · Cited by 202

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 223

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Tu, X., Zhu, K., Luong, N. C., Niyato, D. T., Zhang, Y., & Li, J. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3177522 · Cited by 181

Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges

Ali, M., Karimipour, H., & Tariq, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102355 · Cited by 177

Dynamic Digital Twin and Federated Learning With Incentives for Air-Ground Networks

Sun, W. Y., Xu, N., Wang, L., Zhang, H., & Zhang, Y. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3048137 · Cited by 174

A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective

Lo, S. K., Lu, Q., Wang, C., Paik, H.-Y., & Zhu, L. (2022) · ACM Computing Surveys · doi:10.1145/3450288 · Cited by 202

Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions

Duan, Q., Huang, J., Hu, S., Deng, R., Lu, Z., & Yu, S. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3316615 · Cited by 189

Communication and computation efficiency in Federated Learning: A survey

Almanifi, O. R. A., Chow, C., Tham, M., Chuah, J. H., & Kanesan, J. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100742 · Cited by 180

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Tang, F., Chen, X., Rodrigues, T. K., Zhao, M., & Kato, N. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3197811 · Cited by 165

Towards Fairness-Aware Federated Learning

Shi, Y., Yu, H., & Leung, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3263594 · Cited by 163

GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

Liu, Z., Chen, Y., Yu, H., Liu, Y., & Cui, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501811 · Cited by 149

Incentivizing Differentially Private Federated Learning: A Multidimensional Contract Approach

Wu, M., Ye, D., Ding, J., Guo, Y., Yu, R., & Pan, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3050163 · Cited by 138

Drones’ Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy

Alsamhi, S. H., Almalki, F. A., Afghah, F., Hawbani, A., Shvetsov, A. V., Lee, B., & Song, H. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3132561 · Cited by 139

AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope

Singh, A. P., Satapathy, S. C., Roy, A., & Gutub, A. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06348-2 · Cited by 142

Edge Intelligence: Architectures, Challenges, and ApplicationsPreprint

Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12172 · Cited by 204

An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective

Tang, M., & Wong, V. W. S. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488705 · Cited by 128

Challenges and future directions of secure federated learning: a survey

Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127

Pain-FL: Personalized Privacy-Preserving Incentive for Federated Learning

Sun, P., Che, H., Wang, Z., Wang, Y., Wang, T., Wu, L., & Shao, H. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118354 · Cited by 123

Fairness and privacy preserving in federated learning: A survey

Rafi, T. H., Noor, F. A., Hussain, T., & Chae, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102198 · Cited by 138

Targeted transfer learning through distribution barycenter medium for intelligent fault diagnosis of machines with data decentralization

Yang, B., Lei, Y., Li, X., & Li, N. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122997 · Cited by 117

Backdoor Attacks and Defenses in Federated Learning: State-of-the-Art, Taxonomy, and Future Directions

Gong, X., Chen, Y., Wang, Q., & Kong, W. (2023) · IEEE Wireless Communications · doi:10.1109/mwc.017.2100714 · Cited by 114

Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core

Lee, J., Solat, F., Kim, T. Y., & Poor, H. V. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3352910 · Cited by 125

Multiagent Federated Reinforcement Learning for Secure Incentive Mechanism in Intelligent Cyber–Physical Systems

Xu, M., Peng, J., Gupta, B. B., Kang, J., Xiong, Z., Li, Z., & El‐Latif, A. A. A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3081626 · Cited by 110

Federated Learning With Fair Incentives and Robust Aggregation for UAV-Aided Crowdsensing

Wang, Y., Su, Z., Luan, T. H., Li, R., & Zhang, K. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3138928 · Cited by 98

Blockchain-Enabled Secure and Trusted Federated Data Sharing in IIoT

Zhou, Z., Tian, Y., Xiong, J., Ma, J., & Peng, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3215192 · Cited by 86

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel, V. A., Bhattacharya, P., Tanwar, S., Gupta, R., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3201876 · Cited by 88

Federated Learning and Its Role in the Privacy Preservation of IoT Devices

Alam, T., & Gupta, R. (2022) · Future Internet · doi:10.3390/fi14090246 · Cited by 84

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

Qiao, C., Li, M., Liu, Y., & Tian, Z. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3399612 · Cited by 92

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Ayaz, F., Sheng, Z., Tian, D., & Guan, Y. L. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3132226 · Cited by 79

A review on federated learning towards image processing

Khokhar, F. A., Shah, J. H., Khan, M. A., Sharif, M. I., Tariq, U., & Kadry, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107818 · Cited by 82

Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning

Hu, R., & Gong, Y. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322475 · Cited by 81

Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey

Le, M., Huynh‐The, T., Do‐Duy, T., Vu, T.-H., Hwang, W., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3427324 · Cited by 94

Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation

Deng, Y., Lyu, F., Ren, J., Chen, Y., Yang, P., Zhou, Y., & Zhang, Y. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3195207 · Cited by 79

A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

Teimoori, Z., & Yassine, A. (2022) · Sustainability · doi:10.3390/su142114100 · Cited by 72

Collective Deep Reinforcement Learning for Intelligence Sharing in the Internet of Intelligence-Empowered Edge Computing

Tang, Q., Xie, R., Yu, F. R., Chen, T., Zhang, R., Huang, T., & Liu, Y. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3199812 · Cited by 68

Auction based Incentive Design for Efficient Federated Learning in Cellular Wireless Networks

Le, T. H. T., Tran, N. H. K., Tun, Y. K., Han, Z., & Hong, C. S. (2020) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc45663.2020.9120773 · Cited by 63

An Incentive Auction for Heterogeneous Client Selection in Federated Learning

Pang, J., Yu, J., Zhou, R., & Lui, J. C. S. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3182876 · Cited by 65

Lightweight Digital Twin and Federated Learning With Distributed Incentive in Air-Ground 6G Networks

Sun, W. Y., Lian, S., Zhang, H., & Zhang, Y. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3217923 · Cited by 61

Contract-Theory-Based Incentive Mechanism for Federated Learning in Health CrowdSensing

Li, L., Yu, X., Cai, X., He, X., & Liu, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3218008 · Cited by 63

Faithful Edge Federated Learning: Scalability and Privacy

Zhang, M., Wei, E., & Berry, R. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118423 · Cited by 61

Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning

Liu, J., Chen, C., Li, Y., Sun, L., Song, Y., Zhou, J., Jing, B., & Dou, D. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02117-3 · Cited by 71

Federated Anomaly Analytics for Local Model Poisoning Attack

Shi, S., Hu, C., Wang, D., Zhu, Y., & Han, Z. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118347 · Cited by 59

A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning

Sun, P., Chen, X., Liao, G., & Huang, J. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796833 · Cited by 59

FedDP: A Privacy-Protecting Theft Detection Scheme in Smart Grids Using Federated Learning

Ashraf, M. M., Waqas, M. M., Abbas, G., Baker, T., Abbas, Z. H., & Alasmary, H. (2022) · Energies · doi:10.3390/en15176241 · Cited by 56

Automatic Double-Auction Mechanism for Federated Learning Service Market in Internet of Things

Mai, T., Yao, H., Xu, J., Zhang, N., Liu, Q., & Guo, S. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3170336 · Cited by 54

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

Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond

Berkani, M. R. A., Chouchane, A., Himeur, Y., Ouamane, A., Miniaoui, S., Atalla, S., Mansoor, W., & Al-Ahmad, H. (2025) · Computers · doi:10.3390/computers14040124 · Cited by 64

Adaptive Federated Learning for Digital Twin Driven Industrial Internet of Things

Song, Q., Lei, S., Sun, W. Y., & Zhang, Y. (2021) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc49053.2021.9417370 · Cited by 51

Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

Zaw, C. W., Pandey, S. R., Kim, K. T., & Hong, C. S. (2021) · IEEE Access · doi:10.1109/access.2021.3055523 · 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

Asynchronous federated learning with directed acyclic graph-based blockchain in edge computing: Overview, design, and challenges

Ko, S., Lee, K., Cho, H., Hwang, Y., & Jang, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119896 · Cited by 51

Horizontal Federated Learning of Takagi–Sugeno Fuzzy Rule-Based Models

Zhu, X., Wang, D., Pedrycz, W., & Li, Z. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3118733 · Cited by 44

Double Insurance: Incentivized Federated Learning with Differential Privacy in Mobile Crowdsensing

Ying, C., Jin, H., Wang, X., & Luo, Y. (2020) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds51746.2020.00016 · Cited by 44

Socially-Aware-Clustering-Enabled Federated Learning for Edge Networks

Khan, L. U., Han, Z., Niyato, D. T., & Hong, C. S. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3090446 · Cited by 42

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

A Joint Communication and Learning Framework for Hierarchical Split Federated Learning

Khan, L. U., Guizani, M., Al‐Fuqaha, A., Hong, C. S., Niyato, D. T., & Han, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3315673 · Cited by 40

A Personalized Privacy Preserving Mechanism for Crowdsourced Federated Learning

Xu, Y., Xiao, M., Wu, J., Tan, H., & Gao, G. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3237636 · Cited by 42

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

Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni, A., Ghazzai, H., Alelyani, T., & Massoud, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086410 · Cited by 38

Self-Organizing Democratized Learning: Toward Large-Scale Distributed Learning Systems

Nguyen, M. N. H., Pandey, S. R., Dang, T. N., Huh, E., Tran, N. H. K., Saad, W., & Hong, C. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3170872 · Cited by 45

A game-theoretic approach for federated learning: A trade-off among privacy, accuracy and energy

Yin, L., Lin, S., Sun, Z., Li, R., He, Y., & Hao, Z. (2023) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.12.024 · Cited by 39

An Efficient Incentive Mechanism for Federated Learning in Vehicular Networks

Qiao, C., Zeng, Y., Lu, H. D., Liu, Y., & Tian, Z. (2023) · IEEE Network · doi:10.1109/mnet.2023.3329366 · Cited by 36

Federated Learning-Assisted Vehicular Edge Computing: Architecture and Research Directions

Zhang, X., Liu, J., Hu, T., Chang, Z., Zhang, Y., & Min, G. (2023) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2023.3297793 · Cited by 36

Open problems in medical federated learning

Yoo, J. H., Jeong, H., Lee, J., & Chung, T. (2022) · International Journal of Web Information Systems · doi:10.1108/ijwis-04-2022-0080 · Cited by 40

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

An Exploratory Analysis on Users’ Contributions in Federated Learning

Huang, J., Talbi, R., Zhao, Z., Bouchenak, S., Chen, L. Y., & Roos, S. (2020) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa50397.2020.00014 · Cited by 35

Edge-Assisted Democratized Learning Toward Federated Analytics

Pandey, S. R., Nguyen, M. N. H., Dang, T. N., Tran, N. H. K., Thar, K., Han, Z., & Hong, C. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3085429 · Cited by 35

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