Federated Learning for IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534

Untitled work

(2014) · Cited by 33,419

Are we ready for autonomous driving? The KITTI vision benchmark suite

Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897

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

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204

Overcoming catastrophic forgetting in neural networks

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772

Internet of Things for Smart Cities

Zanella, A., Bui, N., Castellani, A. P., Vangelista, L., & Zorzi, M. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2306328 · Cited by 6,385

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322

Federated learning: Challenges, methods, and future directions

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210

Emerging Properties in Self-Supervised Vision Transformers

Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638

The future of digital health with federated learning

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015

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

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang, C., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., et al. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3249835 · Cited by 2,613

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565

Breast Cancer Statistics, 2022

Giaquinto, A. N., Sung, H., Miller, K. D., Kramer, J. L., Newman, L. A., Minihan, A. K., Jemal, A., & Siegel, R. L. (2022) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21754 · Cited by 2,473

Privacy-preserving deep learning

Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232

Federated Learning with Non-IID Data

Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077

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

Model-Contrastive Federated Learning

Li, Q., He, B., & Song, D. X. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01057 · Cited by 1,738

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv · doi:10.48550/arxiv.1910.06378 · Cited by 1,734

Machine learning for medical imaging

Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672

Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

Phong, L. T., Aono, Y., Hayashi, T., Wang, L., & Moriai, S. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2787987 · Cited by 1,662

An Empirical Study of Training Self-Supervised Vision Transformers

Chen, X., Xie, S., & He, K. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00950 · Cited by 1,648

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

Graph neural network for traffic forecasting: A survey

Jiang, W., & Luo, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117921 · Cited by 1,485

A survey on security and privacy of federated learning

Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480

6G and Beyond: The Future of Wireless Communications Systems

Akyildiz, I. F., Kak, A., & Nie, S. (2020) · IEEE Access · doi:10.1109/access.2020.3010896 · Cited by 1,472

Transformer in Transformer

Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., & Wang, Y. (2021) · arXiv · doi:10.48550/arxiv.2103.00112 · Cited by 1,394

BEiT: BERT Pre-Training of Image Transformers

Bao, H., Dong, L., Piao, S., & Wei, F. (2021) · arXiv · doi:10.48550/arxiv.2106.08254 · Cited by 1,392

Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

Sattler, F., Muller, K.-R., & Samek, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015958 · Cited by 1,355

Federated Multi-Task LearningPreprint

Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10467 · Cited by 1,282

Untitled work

(2015) · Cited by 1,279

Federated Learning for Mobile Keyboard Prediction

Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Yin, D., Chen, Y., Ramchandran, K., & Bartlett, P. (2018) · arXiv · doi:10.48550/arxiv.1803.01498 · Cited by 1,130

Optimizing Federated Learning on Non-IID Data with Reinforcement Learning

Wang, H., Kaplan, Z., Niu, D., & Li, B. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155494 · Cited by 1,054

Federated Learning with Matched AveragingPreprint

Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., & Khazaeni, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06440 · Cited by 1,036

Client-Edge-Cloud Hierarchical Federated Learning

Liu, L., Zhang, J., Song, S. H., & Letaief, K. B. (2020) · ICC 2020 - 2020 IEEE International Conference on Communications (ICC) · doi:10.1109/icc40277.2020.9148862 · Cited by 1,033

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800286 · Cited by 1,030

FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Cao, X., Fang, M., Liu, J., & Gong, N. Z. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24434 · Cited by 999

Federated learning of predictive models from federated Electronic Health Records

Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.007 · Cited by 997

Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory

Kang, J., Xiong, Z., Niyato, D. T., Xie, S., & Zhang, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2940820 · Cited by 961

An Efficient Framework for Clustered Federated Learning

Ghosh, A., Chung, J., Yin, D., & Ramchandran, K. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3192506 · Cited by 956

Federated Learning over Wireless Networks: Optimization Model Design and Analysis

Tran, N. H., Bao, W., Zomaya, A. Y., Nguyen, M. N. H., & Hong, C. (2019) · IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737464 · Cited by 943

LEAF: A Benchmark for Federated Settings

Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.01097 · Cited by 894

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

Blockchained On-Device Federated Learning

Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2921755 · Cited by 887

Swarm Learning for decentralized and confidential clinical machine learning

Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03583-3 · Cited by 884

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796

Federated learning with hierarchical clustering of local updates to improve training on non-IID data

Briggs, C., Fan, Z., & András, P. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207469 · Cited by 786

A Survey on Federated Learning for Resource-Constrained IoT Devices

Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774

Deep Reinforcement Learning: An Overview

Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762

Ensemble Distillation for Robust Model Fusion in Federated LearningPreprint

Lin, T., Kong, L., Stich, S. U., & Jaggi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07242 · Cited by 761

Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2973651 · Cited by 733

Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991401 · Cited by 712

Applied Federated Learning: Improving Google Keyboard Query Suggestions

Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., & Beaufays, F. (2018) · arXiv · doi:10.48550/arxiv.1812.02903 · Cited by 684

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924243 · Cited by 678

SecureBoost: A Lossless Federated Learning Framework

Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q. (2021) · IEEE Intelligent Systems · doi:10.1109/mis.2021.3082561 · Cited by 669

FedML: A Research Library and Benchmark for Federated Machine LearningPreprint

He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13518 · Cited by 668

Model Pruning Enables Efficient Federated Learning on Edge Devices

Jiang, Y., Wang, S., Valls, V., Ko, B. J., Lee, W., Leung, K. K., & Tassiulas, L. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3166101 · Cited by 667

Do Vision Transformers See Like Convolutional Neural Networks?

Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., & Dosovitskiy, A. (2021) · arXiv · doi:10.48550/arxiv.2108.08810 · Cited by 663

Agnostic Federated LearningPreprint

Mohri, M., Sivek, G., & Suresh, A. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00146 · Cited by 652

A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions

Yin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638

Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges

Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625

Flower: A Friendly Federated Learning Research Framework

Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Li, K. H., Parcollet, T., de Gusmão, P. P. B., et al. (2020) · arXiv · doi:10.48550/arxiv.2007.14390 · Cited by 622

Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning

Shejwalkar, V., & Houmansadr, A. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24498 · Cited by 615

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613

Privacy-Preserving Federated Brain Tumour Segmentation

Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578

Short-Term Prediction of Traffic Volume in Urban Arterials

Hamed, M. M., Al‐Masaeid, H. R., & Said, Z. M. B. (1995) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(1995)121:3(249 · Cited by 551

Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges

Pokhrel, S. R., & Choi, J. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2990686 · Cited by 531

Exploration in deep reinforcement learning: A survey

Ladosz, P., Weng, L., Kim, M., & Oh, H. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.003 · Cited by 525

HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous ClientsPreprint

Diao, E., Ding, J., & Tarokh, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01264 · Cited by 499

Model aggregation techniques in federated learning: A comprehensive survey

Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., & Piccialli, F. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.008 · Cited by 496

CMFL: Mitigating Communication Overhead for Federated Learning

Wang, L., Wang, W., & Li, B. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00099 · Cited by 494

Communication-efficient edge ai: algorithms and systems

Shi, Y., Yang, K., Jiang, T., Zhang, J., & Letaief, K. B. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3007787 · Cited by 488

Fair Resource Allocation in Federated Learning

Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2019) · arXiv · doi:10.48550/arxiv.1905.10497 · Cited by 475

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

Fraga‐Lamas, P., Lopes, S. I., & Fernández‐Caramés, T. M. (2021) · Sensors · doi:10.3390/s21175745 · Cited by 465

A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services

Stellios, I., Kotzanikolaou, P., Psarakis, M., Alcaraz, C., & López, J. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2855563 · Cited by 457

Privacy-Preserving Ridge Regression on Hundreds of Millions of Records

Nikolaenko, V., Weinsberg, U., Ioannidis, S., Jóye, M., Boneh, D., & Taft, N. (2013) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2013.30 · Cited by 447

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103291 · Cited by 437

Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

Mills, J., Hu, J., & Min, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2956615 · Cited by 436

Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks

Savazzi, S., Nicoli, M., & Rampa, V. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2964162 · Cited by 431

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., & Thorne, B. (2017) · arXiv · doi:10.48550/arxiv.1711.10677 · Cited by 431

Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

Abad, M. S. H., Ozfatura, E., GUndUz, D., & Ercetin, O. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054634 · Cited by 424

Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework

Wu, Q., He, K., & Chen, X. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2993259 · Cited by 424

Attack of the tails: Yes, you really can backdoor federated learning

Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406

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

A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology

Singh, S., Rathore, S., Alfarraj, O., Tolba, A. K. R., & Yoon, B. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.028 · Cited by 391

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

Almalki, F. A., Alsamhi, S. H., Sahal, R., Hassan, J., Hawbani, A., Rajput, N. S., Saif, A., Morgan, J., & Breslin, J. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-021-01790-w · Cited by 376

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Fu, L., Zhang, H., Gao, G., Zhang, M., & Liu, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299573 · Cited by 343

Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-IID private data

Itahara, S., Nishio, T., Koda, Y., Morikura, M., & Yamamoto, K. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3070013 · Cited by 338

A generic framework for privacy preserving deep learning

Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018) · arXiv · doi:10.48550/arxiv.1811.04017 · Cited by 335

Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

Yu, Z., Hu, J., Min, G., Zhao, Z., Miao, W., & Hossain, M. S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3017474 · Cited by 332

Internet of Things: Security and Solutions Survey

Sadhu, P. K., Yanambaka, V. P., & Abdelgawad, A. (2022) · Sensors · doi:10.3390/s22197433 · Cited by 332

A Performance Evaluation of Federated Learning Algorithms

Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M. (2018) · Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning · doi:10.1145/3286490.3286559 · Cited by 328

Federated learning for smart cities: A comprehensive survey

Pandya, S., Srivastava, G., Jhaveri, R. H., Babu, M. R., Bhattacharya, S., Maddikunta, P. K. R., Mastorakis, S., Piran, M. J., & Gadekallu, T. R. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102987 · Cited by 317

Group Knowledge Transfer: Federated Learning of Large CNNs at the EdgePreprint

He, C., Annavaram, M., & Avestimehr, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.14513 · Cited by 316

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Caldas, S., Konečny, J., McMahan, H. B., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.07210 · Cited by 312

Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

Chen, Y., Liu, Z., Zhang, Y., Wu, Y., Chen, X., & Zhao, L. Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3028963 · Cited by 303

Industrial IoT in 5G-and-Beyond Networks: Vision, Architecture, and Design Trends

Mahmood, A., Beltramelli, L., Abedin, S. F., Zeb, S., Mowla, N. I., Hassan, S. A., Sisinni, E., & Gidlund, M. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3115697 · Cited by 301

Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions

Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103663 · Cited by 301

Edge Intelligence in Intelligent Transportation Systems: A Survey

Gong, T., Zhu, L., Yu, F. R., & Tang, T. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3275741 · Cited by 297

Toward Transformer-Based Object DetectionPreprint

Beal, J., Kim, E. S., Tzeng, E., Park, D. H., Zhai, A., & Kislyuk, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09958 · Cited by 286

FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive

Bao, X., Su, C., Xiong, Y. M., Huang, W., & Hu, Y. (2019) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom.2019.00030 · Cited by 238

Cost-Effective Federated Learning Design

Luo, B., Li, X., Wang, S., Huang, J., & Tassiulas, L. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488679 · Cited by 238

A Fairness-aware Incentive Scheme for Federated Learning

Yu, H., Liu, Z., Liu, Y., Chen, T., Cong, M., Weng, X., Niyato, D. T., & Yang, Q. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375840 · Cited by 236

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Qu, L., Zhou, Y., Liang, P. P., Xia, Y., Wang, F., Adeli, E., Fei-Fei, L., & Rubin, D. L. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00982 · Cited by 235

Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications

Zhou, X., Ye, X., Wang, K. I., Liang, W., Nair, N. C., Shimizu, S., Yan, Z., & Jin, Q. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3259431 · Cited by 235

Practical Federated Gradient Boosting Decision Trees

Li, Q., Wen, Z., & He, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5895 · Cited by 221

Federated Learning in Vehicular Networks

Elbir, A. M., Soner, B., Çöleri, S., Gündüz, D., & Bennis, M. (2022) · IEEE International Mediterranean Conference on Communications and Networking (MeditCom) · doi:10.1109/meditcom55741.2022.9928621 · Cited by 218

Rethinking Federated Learning with Domain Shift: A Prototype View

Huang, W., Ye, M., Shi, Z., He, L., & Du, B. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01565 · Cited by 217

Gossip Learning as a Decentralized Alternative to Federated Learning

Hegedűs, I., Danner, G., & Jelasity, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22496-7_5 · Cited by 216

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

Albreem, M. A. M., Sheikh, A. M., Alsharif, M. H., Jusoh, M., & Yasin, M. N. M. (2021) · IEEE Access · doi:10.1109/access.2021.3061697 · Cited by 211

Towards Building The Federatedgpt: Federated Instruction Tuning

Zhang, J., Vahidian, S., Kuo, M., Li, C., Zhang, R., Yu, T., Wang, G., & Chen, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447454 · Cited by 207

Federated Learning for Breast Density Classification: A Real-World Implementation

Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60548-3_18 · Cited by 202

Machine learning meets quantum physics

Sarma, S. D., Deng, D.-L., & Duan, L.-M. (2019) · Physics Today · doi:10.1063/pt.3.4164 · Cited by 201

Collaborative Fairness in Federated Learning

Lyu, L., Xu, X., Wang, Q., & Yu, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63076-8_14 · Cited by 200

FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting

Zhang, C., Zhang, S., Yu, J. J. Q., & Yu, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3055283 · Cited by 194

Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

Yang, W., Xiang, W., Yang, Y. W., & Cheng, P. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3183465 · Cited by 192

Distributed optimization with arbitrary local solvers

Ma, C., Konečný, J., Jaggi, M., Smith, V., Jordan, M. I., Richtárik, P., & Takáč, M. (2017) · Optimization methods & software · doi:10.1080/10556788.2016.1278445 · Cited by 191

Design and Analysis of Uplink and Downlink Communications for Federated Learning

Zheng, S., Shen, C., & Chen, X. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3041388 · Cited by 191

DRACO: Byzantine-resilient Distributed Training via Redundant GradientsPreprint

Chen, L., Wang, H., Charles, Z., & Papailiopoulos, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09877 · Cited by 191

Training data-efficient image transformers & distillation through attention

Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2020) · arXiv · doi:10.48550/arxiv.2012.12877 · Cited by 182

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

Sustainability of Healthcare Data Analysis IoT-Based Systems Using Deep Federated Learning

Elayan, H., Aloqaily, M., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3103635 · Cited by 176

Generalized Byzantine-tolerant SGDPreprint

Xie, C., Koyejo, O. O., & Gupta, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10116 · Cited by 172

Federated learning in smart cities: Privacy and security survey

Al-Huthaifi, R., Li, T., Huang, W. X., Gu, J., & Li, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.033 · Cited by 165

FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments

Rajagopal, S. M., Supriya, M., & Buyya, R. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100784 · Cited by 162

A Survey on Security, Privacy, Trust, and Architectural Challenges in IoT Systems

Adam, M., Hammoudeh, M. A. A., Alrawashdeh, R., & Alsulaimy, B. (2024) · IEEE Access · doi:10.1109/access.2024.3382709 · Cited by 158

Federated Learning with Class Imbalance Reduction

Yang, M., Wang, X., Zhu, H., Wang, H., & Qian, H. L. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616052 · Cited by 155

Sample-level Data Selection for Federated Learning

Li, A., Zhang, L., Tan, J., Qin, Y., Wang, J., & Li, X. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488723 · Cited by 153

Freeway Performance Measurement System: Operational Analysis Tool

Choe, T., Skabardonis, A., & Varaiya, P. (2002) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1811-08 · Cited by 152

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered DropoutPreprint

Horváth, S., Laskaridis, S., Almeida, M., Leontiadis, I., Venieris, S. I., & Lane, N. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.13451 · Cited by 151

Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems

Manias, D. M., & Shami, A. A. (2021) · IEEE Network · doi:10.1109/mnet.011.2000552 · Cited by 148

Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm

Qu, X., Wang, S., Hu, Q., & Cheng, X. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3056773 · Cited by 144

FedBERT : When Federated Learning Meets Pre-training

Tian, Y., Wan, Y., Lyu, L., Yao, D., Jin, H., & Sun, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3510033 · Cited by 144

FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

Tang, M., Ning, X., Wang, Y., Sun, J., Wang, Y., Li, H. H., & Chen, Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00986 · Cited by 144

FedMask: Joint Computation and Communication-Efficient Personalized Federated Learning via Heterogeneous Masking

Li, A., Sun, J., Zeng, X., Zhang, M., Li, H., & Chen, Y. (2021) · Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/3485730.3485929 · Cited by 139

FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

Ruan, Y., & Joe‐Wong, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20785 · Cited by 134

FedTP: Federated Learning by Transformer Personalization

Li, H., Cai, Z., Wang, J., Tang, J., Ding, W., Lin, C., & Shi, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3269062 · Cited by 134

FedBoost: A Communication-Efficient Algorithm for Federated Learning

Hamer, J., Mohri, M., & Suresh, A. T. (2020) · Cited by 133

Federated Transfer Reinforcement Learning for Autonomous Driving

Liang, X., Liu, Y., Chen, T., Liu, M., & Yang, Q. (2022) · Adaptation, learning, and optimization · doi:10.1007/978-3-031-11748-0_15 · Cited by 131

BDFL: A Byzantine-Fault-Tolerance Decentralized Federated Learning Method for Autonomous Vehicle

Chen, J.-H., Chen, M.-R., Zeng, G., & Weng, J. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3102121 · Cited by 130

The Policy Effect of the General Data Protection Regulation (GDPR) on the Digital Public Health Sector in the European Union: An Empirical Investigation

Yuan, B., & Li, J. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16061070 · Cited by 125

FogFL: Fog-Assisted Federated Learning for Resource-Constrained IoT Devices

Saha, R., Misra, S., & Deb, P. K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3046509 · Cited by 125

Federated Transfer Learning for Bearing Fault Diagnosis With Discrepancy-Based Weighted Federated Averaging

Chen, J., Li, J., Huang, R., Yue, K., Chen, Z., & Li, W. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3180417 · Cited by 125

Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey

Pfeiffer, K., Rapp, M., Khalili, R., & Henkel, J. (2023) · ACM Computing Surveys · doi:10.1145/3596907 · Cited by 125

Efficient and flexible management for industrial Internet of Things: A federated learning approach

Guo, Y., Zhao, Z., He, K., Lai, S., Xia, J., & Fan, L. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108122 · Cited by 122

FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models

Zhang, Z., Yang, Y., Dai, Y., Wang, Q., Yu, Y., Qu, L., & Xu, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.632 · Cited by 121

Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Cho, Y. J., Manoel, A., Joshi, G., Sim, R., & Dimitriadis, D. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/399 · Cited by 119

FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning

Xiong, Y., Wang, R., Cheng, M., Yu, F., & Hsieh, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01566 · Cited by 119

Framework for Detecting Breast Cancer Risk Presence Using Deep Learning

Humayun, M., Khalil, M. I., Almuayqil, S. N., & Jhanjhi, N. Z. (2023) · Electronics · doi:10.3390/electronics12020403 · Cited by 118

Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway

Hua, G., Zhu, L., Wu, J., Shen, C., Zhou, L., & Lin, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3021253 · Cited by 115

Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better

Bibikar, S., Vikalo, H., Wang, Z., & Chen, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i6.20555 · Cited by 114

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt, L., Heyer, M., Toyoda, K., Samek, W., & Li, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3231363 · Cited by 114

Federated Learning of Gboard Language Models with Differential Privacy

Xu, Z., Zhang, Y., Andrew, G., Choquette-Choo, C. A., Kairouz, P., McMahan, H. B., Rosenstock, J., & Zhang, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.60 · Cited by 114

Decentralized federated learning for extended sensing in 6G connected vehicles

Barbieri, L., Savazzi, S., Brambilla, M., & Nicoli, M. (2021) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100396 · Cited by 113

On Demand Fog Federations for Horizontal Federated Learning in IoV

Hammoud, A., Otrok, H., Mourad, A., & Dziong, Z. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3172370 · Cited by 110

Does Knowledge Distillation Really Work?Preprint

Stanton, S., Izmailov, P., Kirichenko, P., Alemi, A. A., & Wilson, A. G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05945 · Cited by 110

Heterogeneity for the Win: One-Shot Federated ClusteringPreprint

Dennis, D. K., Li, T., & Smith, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.00697 · Cited by 105

6G Connected Vehicle Framework to Support Intelligent Road Maintenance Using Deep Learning Data Fusion

Hijji, M., Iqbal, R., Pandey, A. K., Doctor, F., Karyotis, C., Rajeh, W., Alshehri, A., & Aradah, F. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235151 · Cited by 95

PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy

Wang, B., Chen, Y., Jiang, H., & Zhao, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3264259 · Cited by 92

Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT

Zheng, J., Li, K., Mhaisen, N., Ni, W., Tovar, E., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176739 · Cited by 90

OQFL: An Optimized Quantum-Based Federated Learning Framework for Defending Against Adversarial Attacks in Intelligent Transportation Systems

Yamany, W., Moustafa, N., & Turnbull, B. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3130906 · Cited by 87

Multicenter Hierarchical Federated Learning With Fault-Tolerance Mechanisms for Resilient Edge Computing Networks

Chen, X., Xu, G., Xu, X., Jiang, H., Tian, Z., & Ma, T. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3362974 · Cited by 87

Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art

Imteaj, A., Mamun Ahmed, K., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2023) · Adaptation, Learning, and Optimization · doi:10.1007/978-3-031-11748-0_2 · Cited by 86

A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data

Nguyen, T., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., & Perugini, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-12833-x · Cited by 86

Exploiting Unlabeled Data in Smart Cities using Federated Edge Learning

Albaseer, A., Ciftler, B. S., Abdallah, M., & Al-Fuqaha, A. (2020) · 2020 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc48107.2020.9148475 · Cited by 84

Security and Privacy Issues in Autonomous Vehicles: A Layer-Based Survey

Hataba, M., Sherif, A., Abdallah, M. M., Abdallah, M. M., & Alasmary, W. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3169500 · Cited by 82

Decentralized Federated Learning for Electronic Health Records

Lu, S., Zhang, Y., & Wang, Y. (2020) · Conference on Information Sciences and Systems · doi:10.1109/ciss48834.2020.1570617414 · Cited by 81

Mammographic density and breast cancer risk in White and African American Women

Razzaghi, H., Troester, M. A., Gierach, G. L., Olshan, A. F., Yankaskas, B. C., & Millikan, R. C. (2012) · Breast Cancer Research and Treatment · doi:10.1007/s10549-012-2185-3 · Cited by 80

Machine learning and the Internet of Things security: Solutions and open challenges

Farooq, U., Tariq, N., Asim, M., Baker, T., & Al-Shamma'a, A. I. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.01.015 · Cited by 80

Federated learning-based secure electronic health record sharing scheme in medical informatics

Salim, M. M., & Park, J. H. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3174823 · Cited by 80

Free-riders in Federated Learning: Attacks and Defenses

Lin, J., Du, M., & Liu, J. (2019) · arXiv · doi:10.48550/arxiv.1911.12560 · Cited by 76

DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingPreprint

Dai, R., Shen, L., He, F., Tian, X., & Tao, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.00187 · Cited by 74

Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection

Rjoub, G., Wahab, O. A., Bentahar, J., Cohen, R. A., & Bataineh, A. S. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10307-z · Cited by 73

BlockFLA

Desai, H., Özdayi, M. S., & Kantarcıoğlu, M. (2021) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3422337.3447837 · Cited by 72

A Data Sharing Scheme Based on Federated Learning in IoV

Hu, X., Li, R., Wang, L., Ning, Y., & Ota, K. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3266100 · Cited by 72

Throughput-Optimal Topology Design for Cross-Silo Federated LearningPreprint

Marfoq, O., Xu, C., Neglia, G., & Vidal, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12229 · Cited by 71

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

Elijah, O., Rahim, S. K. A., New, W. K., Leow, C. Y., Cumanan, K., & Geok, T. K. (2022) · IEEE Access · doi:10.1109/access.2022.3208284 · Cited by 66

Federated learning for green and sustainable 6G IIoT applications

Khanh, Q. V., Nguyen, D. C., Anh, D. V., & Quý, N. M. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101061 · Cited by 66

Deep Federated Learning for IoT-based Decentralized Healthcare Systems

Elayan, H., Aloqaily, M., & Guizani, M. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498820 · Cited by 61

Adaptive Resource Optimized Edge Federated Learning in Real-Time Image Sensing Classifications

Tam, P., Math, S., Nam, C., & Kim, S. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3120724 · Cited by 61

On the Convergence of Communication-Efficient Local SGD for Federated Learning

Gao, H., Xu, A. P., & Huang, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.16920 · Cited by 59

Age of Information minimization in UAV-aided data collection for WSN and IoT applications: A systematic review

Amodu, O. A., Bukar, U. A., Mahmood, R. A. R., Jarray, C., & Othman, M. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103652 · Cited by 59

A federated learning framework for healthcare IoT devices

Yuan, B., Ge, S., & Xing, W. (2020) · arXiv · doi:10.48550/arxiv.2005.05083 · Cited by 59

On the Importance and Applicability of Pre-Training for Federated LearningPreprint

Chen, H.-Y., Tu, C.-H., Li, Z., Shen, H., & Chao, W. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.11488 · Cited by 58

Performance evaluation of range-free localization algorithms for wireless sensor networks

Nemer, I. A., Sheltami, T., Shakshuki, E., Elkhail, A. A., & Adam, M. (2020) · Personal and Ubiquitous Computing · doi:10.1007/s00779-020-01370-x · Cited by 57

Towards sustainable smart IoT applications architectural elements and design: opportunities, challenges, and open directions

Ali, Z. H., & Ali, H. A. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03477-7 · Cited by 57

Digestive neural networks: A novel defense strategy against inference attacks in federated learning

Lee, H.-K., Kim, J., Ahn, S.-Y., Hussain, R., Cho, S., & Son, J. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102378 · Cited by 57

Elastic Aggregation for Federated Optimization

Chen, D., Hu, J., Tan, V. J., Wei, X., & Wu, E. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01173 · Cited by 56

How to Understand Masked AutoencodersPreprint

Cao, S., Xu, P., & Clifton, D. A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.03670 · Cited by 56

Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228

Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review

Jamshidi, S., Nikanjam, A., Nafi, K. W., Khomh, F., & Rasta, R. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101531 · Cited by 55

Energy, Scalability, Data, and Security in Massive IoT: Current Landscape and Future Directions

Cheikh, I., Roy, S., Sabir, E., & Aouami, R. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3655796 · Cited by 46

Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions

Adam, M., Albaseer, A. M., Baroudi, U. A., & Abdallah, M. M. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3554537 · Cited by 36

Advanced Fault Detection and Diagnosis Exploiting Machine Learning and Artificial Intelligence for Engineering Applications

Paolini, D., Dini, P., Elhanashi, A., & Saponara, S. (2026) · Electronics · doi:10.3390/electronics15020476 · Cited by 30

Enhancing Privacy and Efficiency in IoT through Federated Learning

Shaik, N. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24601193422

The Synergy of Federated Learning and IoT: Pioneering Privacy and Efficiency in Decentralised Systems

Mukund, A., Varun, T. A., & Sundharakumar, K. B. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-98360-3_6

Federated Learning for Internet of Things

Li, Y., Zhang, Q., Wang, X., Zeng, R., Li, H., Murturi, I., Dustdar, S., & Huang, M. (2023) · Springer eBooks · doi:10.1007/978-3-031-50514-0_3 · Cited by 5

Federated Learning on Internet of Things: Extensive and Systematic Review

Aggarwal, M., Khullar, V., Rani, S., Prola, T. A., Bhattacharjee, S. B., Shawon, S. M., & Goyal, N. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.049846 · Cited by 38

Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications

Dritsas, E., & Τρίγκα, Μ. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14010009 · Cited by 177