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

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

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

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141

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

The multimodal brain tumor image segmentation benchmark (BRATS)

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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

The MovieLens Datasets: History and Context

Harper, F., & Konstan, J. (2016) · TIIS · doi:10.1145/2827872 · Cited by 5,938

Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

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Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers

Arbaoui, M., Brahmia, M., Rahmoun, A., & Zghal, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3678182 · Cited by 54

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

Adam, M., & Baroudi, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3506214 · Cited by 46

Privacy-Preserving Continuous Authentication for Mobile and IoT Systems Using Warmup-Based Federated Learning

Wazzeh, M., Ould‐Slimane, H., Talhi, C., Mourad, A., & Guizani, M. (2022) · IEEE Network · doi:10.1109/mnet.121.2200099 · Cited by 46

Secure, Privacy Preserving, and Verifiable Federating Learning Using Blockchain for Internet of Vehicles

Ghimire, B., & Rawat, D. B. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3097705 · Cited by 46

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

Qian, L., Yang, P., Xiao, M., Dobre, O. A., Renzo, M. D., Li, J., Han, Z., Qin, Y., & Zhao, J. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3156756 · Cited by 47

A Study on Multi-Antenna and Pertinent Technologies with AI/ML Approaches for B5G/6G Networks

Siddiqui, M. U. A., Qamar, F., Kazmi, S. H. A., Hassan, R. B., Arfeen, A., & Nguyễn, Q. N. (2022) · Electronics · doi:10.3390/electronics12010189 · Cited by 45

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning

Almadhor, A. S., Sampedro, G. A., Abisado, M., Abbas, S., Kim, Y., Khan, M. A., Baili, J., & Cha, J. (2023) · Sensors · doi:10.3390/s23083984 · Cited by 53

Fed-TSN: Joint Failure Probability-Based Federated Learning for Fault-Tolerant Time-Sensitive Networks

Balasubramanian, V., Aloqaily, M., & Martin Reisslein, B. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3273396 · Cited by 45

Management of Digital Twin-Driven IoT Using Federated Learning

AbdulRahman, S., Otoum, S., Bouachir, O., & Mourad, A. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310102 · Cited by 48

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

FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks

Li, B., Jiang, Y., Pei, Q., Li, T., Liu, L., & Lu, R. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3190294 · Cited by 49

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing

Khalaf, O. I., Ashokkumar, S. R., Algburi, S. S., Anupallavi, S., Dhanasekaran, S., Sharif, M. S., & Elmedany, W. M. (2024) · Security and Privacy · doi:10.1002/spy2.374 · Cited by 44

Hierarchical Federated Learning for Collaborative IDS in IoT Applications

Saadat, H., Aboumadi, A., Mohamed, A. M. S., Erbad, A., & Guizani, M. (2021) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco52532.2021.9460304 · Cited by 45

FedAffect: Few-shot federated learning for facial expression recognition

Shome, D., & Kar, T. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00463 · Cited by 45

IoT‐5G and B5G/6G resource allocation and network slicing orchestration using learning algorithms

Ari, A. A. A., Samafou, F., Njoya, A. N., Djedouboum, A. C., Aboubakar, M., & Mohamadou, A. (2025) · IET Networks · doi:10.1049/ntw2.70002 · Cited by 42

Toward Quantum Federated Learning

Ren, C., Yan, R., Zhu, H., Yu, H., Xu, M., Shen, Y., Xu, Y. H., Xiao, M., Dong, Z. Y., Skoglund, M., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3552643 · Cited by 93

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

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

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

Muhammad, Z., Anwar, Z., Javed, A. R., Saleem, B., Abbas, S., & Gadekallu, T. R. (2023) · Technologies · doi:10.3390/technologies11030076 · Cited by 41

Federated Learning-Oriented Edge Computing Framework for the IIoT

Liu, X., Dong, X., Ning, J., & Zhao, W. (2024) · Sensors · doi:10.3390/s24134182 · Cited by 45

MediSecFed: Private and Secure Medical Image Classification in the Presence of Malicious Clients

Kumar, A., Purohit, V., Bharti, V., Singh, R., & Singh, S. K. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3138919 · Cited by 40

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

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

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Baabdullah, T., Alzahrani, A., Rawat, D. B., & Liu, C. (2024) · Future Internet · doi:10.3390/fi16060196 · Cited by 57

Security of Federated Learning: Attacks, Defensive Mechanisms, and Challenges

Benmalek, M., Benrekia, M. A., & Challal, Y. (2022) · Revue d intelligence artificielle · doi:10.18280/ria.360106 · Cited by 39

On-Demand-FL: A Dynamic and Efficient Multicriteria Federated Learning Client Deployment Scheme

Chahoud, M., Sami, H., Mourad, A., Otoum, S., Otrok, H., Bentahar, J., & Guizani, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3265564 · Cited by 39

FedFM: Anchor-Based Feature Matching for Data Heterogeneity in Federated Learning

Ye, R., Ni, Z., Xu, C., Wang, J., Chen, S., & Eldar, Y. C. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3314277 · Cited by 39

Decentralized Deep-Learning Approach for Lithium-Ion Batteries State of Health Forecasting Using Federated Learning

Wong, K. L., Tse, R., Tang, S.-K., & Pau, G. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3354551 · Cited by 38

Joint Selection of Local Trainers and Resource Allocation for Federated Learning in Open RAN Intelligent Controllers

Singh, A. K., & Nguyen, K. K. (2022) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc51071.2022.9771700 · Cited by 37

Blockchain-Enhanced Federated Learning Market With Social Internet of Things

Wang, P., Zhao, Y., Obaidat, M. S., Wei, Z., Qi, H., Lin, C., Xiao, Y., & Zhang, Q. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3213314 · Cited by 39

Federated and Transfer Learning-Empowered Intrusion Detection for IoT Applications

Otoum, Y., Chamola, V., & Nayak, A. R. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2200048 · Cited by 36

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

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

Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

Ajala, O. A., Arinze, C. A., Ofodile, O. C., Okoye, C. C., & Daraojimba, O. D. (2024) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2024.11.1.0060 · Cited by 36

A comprehensive review on Federated Learning for Data-Sensitive Application: Open issues & challenges

Narula, M., Meena, J., & Vishwakarma, D. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108128 · Cited by 38

Decentralized and Distributed Learning for AIoT: A Comprehensive Review, Emerging Challenges, and Opportunities

Xu, H., Seng, K. P., Ang, L.-M., & Smith, J. (2024) · IEEE Access · doi:10.1109/access.2024.3422211 · Cited by 42

Toward Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

Chen, X., Li, Z., Ni, W., Wang, X., Zhang, S., Sun, Y., Xu, S., & Pei, Q. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3420733 · Cited by 36

Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning

Wardana, A. A., & Sukarno, P. (2024) · ACM Computing Surveys · doi:10.1145/3701724 · Cited by 43

Comprehensive review of federated learning challenges: a data preparation viewpoint

Saeed, N., Ashour, M., & Mashaly, M. E. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01195-6 · Cited by 43

FedMint: Intelligent Bilateral Client Selection in Federated Learning With Newcomer IoT Devices

Wehbi, O., Arisdakessian, S., Wahab, O. A., Otrok, H., Otoum, S., Mourad, A., & Guizani, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3283855 · Cited by 36

Recent Advancements in Federated Learning: State of the Art, Fundamentals, Principles, IoT Applications and Future Trends

Papadopoulos, C., Kollias, K.-F., & Fragulis, G. F. (2024) · Future Internet · doi:10.3390/fi16110415 · Cited by 35

Wireless for Machine Learning: A Survey

Hellström, H., da Silva, J. M. B., Amiri, M. M., Chen, M., Fodor, V., Poor, H. V., & Fischione, C. (2022) · doi:10.1561/9781638280071 · Cited by 55

A Survey on Collaborative Learning for Intelligent Autonomous Systems

dos Anjos, J. C. S., Matteussi, K. J., Orlandi, F. C., Barbosa, J. L. V., Silva, J. S., Bittencourt, L. F., & Geyer, C. F. R. (2023) · ACM Computing Surveys · doi:10.1145/3625544 · Cited by 38

On the Performance of Federated Learning Algorithms for IoT

Tahir, M., & Ali, M. I. (2022) · IoT · doi:10.3390/iot3020016 · Cited by 35

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

Luo, J., Hu, Y., & Bai, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3092192 · Cited by 37

FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

Younis, R., & Fisichella, M. (2022) · IEEE Access · doi:10.1109/access.2022.3184309 · Cited by 34

When machine learning meets Network Management and Orchestration in Edge-based networking paradigms

Shahraki, A., Ohlenforst, T., & Kreyß, F. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103558 · Cited by 35

Data independent warmup scheme for non-IID federated learning

Arafeh, M., Ould‐Slimane, H., Otrok, H., Mourad, A., Talhi, C., & Damiani, E. (2022) · Information Sciences · doi:10.1016/j.ins.2022.12.045 · Cited by 34

Federated learning vulnerabilities, threats and defenses: A systematic review and future directions

Almutairi, S., & Barnawi, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100947 · Cited by 35

Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement

Shiri, I., Salimi, Y., Maghsudi, M., Jenabi, E., Harsini, S., Razeghi, B., Mostafaei, S., Hajianfar, G., Sanaat, A., Jafari, E., et al. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06418-7 · Cited by 33

Edge-Federated Learning-Based Intelligent Intrusion Detection System for Heterogeneous Internet of Things

Mahadik, S. S., Pawar, P. M., & Muthalagu, R. (2024) · IEEE Access · doi:10.1109/access.2024.3410046 · Cited by 35

Distributed intelligence for IoT-based smart cities: a survey

Hashem, M., Siddiqa, A., Alaba, F. A., Bilal, M., & Alhashmi, S. M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10136-y · Cited by 33

Deep-Learning-Based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities

Temiz, M., Zhang, Y., Fu, Y., Zhang, C., Meng, C., Kaplan, O., & Masouros, C. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3586560 · Cited by 40

A survey on federated learning applications in healthcare, finance, and data privacy/data security

Nevrataki, T., Iliadou, A., Ntolkeras, G., Sfakianakis, I., Lazaridis, L., Maraslidis, G., Asimopoulos, N., & Fragulis, G. F. (2023) · AIP Conference Proceedings · doi:10.1063/5.0182160 · Cited by 32

FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks

Khan, R., Saeed, U., & Koo, I. (2024) · Electronics · doi:10.3390/electronics13244907 · Cited by 32

Federated and transfer learning for cancer detection based on image analysis

Bechar, A., Medjoudj, R., Elmir, Y., Himeur, Y., & Amira, A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10956-y · Cited by 38

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

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

A review on application of machine learning-based methods for power system inertia monitoring

Heidari, M., Ding, L., Kheshti, M., Bao, W., Zhao, X., Popov, M., & Terzija, V. V. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.110279 · Cited by 32

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

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

Federated learning-based natural language processing: a systematic literature review

Khan, Y., Sánchez, D., & Domingo‐Ferrer, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10970-5 · Cited by 35

Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks

Shen, Y., Gou, F., & Wu, J. (2022) · Mathematics · doi:10.3390/math10101669 · Cited by 29

Distributed Deep Learning and Energy-Efficient Real-Time Image Processing at the Edge for Fish Segmentation in Underwater Videos

Jahanbakht, M., Xiang, W., Waltham, N. J., & Azghadi, M. R. (2022) · IEEE Access · doi:10.1109/access.2022.3202975 · Cited by 29

Application of Federated Learning Techniques for Arrhythmia Classification Using 12-Lead ECG Signals

Jimenez-Gutierrez, D. M., Hassan, H. M., Landi, L., Vitaletti, A., & Chatzigiannakis, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49361-4_3 · Cited by 37

Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

Janbi, N. F., Katib, I. A., & Mehmood, R. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200231 · Cited by 30

Proof-of-authority-based secure and efficient aggregation with differential privacy for federated learning in industrial IoT

Putra, M. A. P., Alief, R. N., Rachmawati, S. M., Sampedro, G. A., Kim, D., & Lee, J. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101107 · Cited by 31

A federated learning-enabled predictive analysis to forecast stock market trends

Ardakani, S. P., Du, N., Lin, C., Yang, J.-C., Bi, Z., & Chen, L. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04570-4 · Cited by 28

The Effectiveness of Multimedia-Based Learning To Accelerate Learning After The Pandemic At The Basic Education Level

Tuhuteru, L., Misnawati, D., Aslan, A., Taufiqoh, Z., & Imelda, I. (2023) · Tafkir Interdisciplinary Journal of Islamic Education · doi:10.31538/tijie.v4i1.311 · Cited by 28

A systematic review of privacy techniques in recommendation systems

Ogunseyi, T. B., Avoussoukpo, C. B., & Jiang, Y. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00710-1 · Cited by 27

Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things

Abbas, S., Sampedro, G. A., Abisado, M., Almadhor, A. S., Yousaf, I., & Hong, S.-P. (2023) · Electronics · doi:10.3390/electronics12122612 · Cited by 28

FedTor: An Anonymous Framework of Federated Learning in Internet of Things

Chen, Y., Su, Y., Zhang, M., Chai, H., Wei, Y., & Yu, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3162826 · Cited by 28

ModularFed: Leveraging modularity in federated learning frameworks

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

Dispositivos, Eu Escolho Vocês: Seleção de Clientes Adaptativa para Comunicação Eficiente em Aprendizado Federado

Souza, A. M. D., Bittencourt, L. F., Cerqueira, E., Loureiro, A. A. F., & Villas, L. A. (2023) · Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC) · doi:10.5753/sbrc.2023.499 · Cited by 27

FedLC: Optimizing Federated Learning in Non-IID Data via Label-Wise Clustering

Lee, H., & Seo, D. (2023) · IEEE Access · doi:10.1109/access.2023.3271517 · Cited by 31

A Close Look at the Communication Efficiency and the Energy Footprints of Robust Federated Learning in Industrial IoT

Barbieri, L., Kianoush, S., Nicoli, M., Serio, L., & Savazzi, S. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3530265 · Cited by 27

Adaptive Upgrade of Client Resources for Improving the Quality of Federated Learning Model

AbdulRahman, S., Ould‐Slimane, H., Chowdhury, R., Mourad, A., Talhi, C., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3218755 · Cited by 30

Network Selection Over 5G-Advanced Heterogeneous Networks Based on Federated Learning and Cooperative Game Theory

González, C. C., Pupo, E. F., Iradier, E., Angueira, P., Murroni, M., & Montalbán, J. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3373638 · Cited by 26

Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools

Nguyen, G., Díaz, J. S.-P., Calatrava, A., Berberi, L., Lytvyn, O., Kozlov, V. Y., Tran, V., Moltó, G., & García, Á. L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11036-2 · Cited by 32

Pelican Optimization Algorithm with Federated Learning Driven Attack Detection model in Internet of Things environment

Al‐Wesabi, F. N., Mengash, H. A., Marzouk, R., Alruwais, N. M., Allafi, R., Alabdan, R., Alharbi, M., & Gupta, D. K. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.05.029 · Cited by 27

Label synchronization for Hybrid Federated Learning in manufacturing and predictive maintenance

Rosero, R. L., Silva, C., Ribeiro, B., & Santos, B. F. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02298-8 · Cited by 27

Client Selection in Hierarchical Federated Learning

Trindade, S., & da Fonseca, N. L. S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3403082 · Cited by 25

Machine learning algorithms for smart and intelligent healthcare system in Society 5.0

Zamzami, I. F., Pathoee, K., Gupta, B. B., Mishra, A., Rawat, D., & Alhalabi, W. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23061 · Cited by 23

A Review of Federated Meta-Learning and Its Application in Cyberspace Security

Liu, F., Li, M., Liu, X., Xue, T., Ren, J. F., & Zhang, C. (2023) · Electronics · doi:10.3390/electronics12153295 · Cited by 25

A Survey on Self-Evolving Autonomous Driving: A Perspective on Data Closed-Loop Technology

Li, X., Wang, Z., Huang, Y., & Chen, H. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3319689 · Cited by 25

The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

Selvi, G. C., Murugan, R., Yenduri, G., Gurrammagari, D. R., Bhulakshmi, D., Kandati, D. R., Supriya, Y., Gadekallu, T. R., Rathore, R. S., & Jhaveri, R. H. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558266152231128060222 · Cited by 22

Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images

Ahmed, R., Maddikunta, P. K. R., Gadekallu, T. R., Al-Shammari, N. K., & Hendaoui, F. A. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1409314 · Cited by 22

A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks

Dang, X.-T., Vu, B., Nguyen, Q.-S., Tran, T.-D., Eom, J.-S., & Shin, O. (2024) · Energies · doi:10.3390/en17246485 · Cited by 26

DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network

Chen, Z., Li, D., Zhu, J., & Zhang, S. (2022) · Sensors · doi:10.3390/s22093317 · Cited by 23

A novel game theory based reliable proof‐of‐stake consensus mechanism for blockchain

Bala, K., & Kaur, P. D. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4525 · Cited by 22

Regulated Subspace Projection Based Local Model Update Compression for Communication-Efficient Federated Learning

Park, S., & Choi, W. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242722 · Cited by 21

On the Dependability of 6G Networks

Ahmad, I., Yaguache, F. R., Huusko, J., & Seppänen, K. (2023) · Electronics · doi:10.3390/electronics12061472 · Cited by 23

Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks

Manzoor, S. I., Jain, S., Singh, Y. P., & Singh, H. (2023) · IEEE Access · doi:10.1109/access.2023.3269880 · Cited by 22

Low-Overhead Clustered Federated Learning for Personalized Stress Monitoring

Jiang, S., Firouzi, F., & Chakrabarty, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299736 · Cited by 26

A systematic review on federated learning system: a new paradigm to machine learning

Chaudhary, R. K., Kumar, R., & Saxena, N. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02257-6 · Cited by 24

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Vajrobol, V., Singh, S., Saxena, G. J., Pundir, A., Gaurav, A., Bansal, S., Attar, R. W., Rahman, M., & Gupta, B. B. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.056500 · Cited by 26

Enhancing Cybersecurity and Privacy Protection for Cloud Computing-Assisted Vehicular Network of Autonomous Electric Vehicles: Applications of Machine Learning

Yang, T., Sun, R., Rathore, R. S., & Baig, I. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj16010014 · Cited by 23

FedVANET: Efficient Federated Learning with Non-IID Data for Vehicular Ad Hoc Networks

Li, B., Jiang, Y., Sun, W., Niu, W., & Wang, P. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685068 · Cited by 22

Serverless Computing Approach for Deploying Machine Learning Applications in Edge Layer

Bac, T. P., Tran, M. N., & Kim, Y. (2022) · International Conference on Information Networking · doi:10.1109/icoin53446.2022.9687209 · Cited by 21

Towards federated learning: An overview of methods and applications

Silva, P. R., Vinagre, J., & da Gama, J. M. P. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1486 · Cited by 27

Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

Salama, A., Stergioulis, A., Hayajneh, A. M., Zaidi, S. A. R., McLernon, D., & Robertson, I. D. (2023) · IEEE Access · doi:10.1109/access.2023.3246924 · Cited by 24

Performance Optimization for Energy-Efficient Industrial Internet of Things Based on Ambient Backscatter Communication: An A3C-FL Approach

Huang, Y., Li, M., Yu, F. R., Si, P., & Zhang, Y. (2023) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2023.3260199 · Cited by 21

Reliable federated learning in a cloud-fog-IoT environment

Sharma, M., & Kaur, P. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05252-w · Cited by 21

Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning

Albaseer, A. M., Abdallah, M. M., Al‐Fuqaha, A., Seid, A. M., Erbad, A., & Dobre, O. A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3270168 · Cited by 22

Federated learning: Challenges and future work

Hossain, M. B., & Rahman, R. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.15.2.0711 · Cited by 5

Towards_Scalable_and_Secure_Federated_Learning__Key_Issues_and_Future_Research.pdf

Daniels, M., Gallus, S., & Wood, R. (2025) · Figshare · doi:10.6084/m9.figshare.28238567

Federated Learning: A Survey on Privacy-Preserving Collaborative IntelligencePreprint

Rahman, R. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2504.17703 · Cited by 3

Federated Learning With Deep Learning: A Comprehensive Survey

Mnkash, S. H., Alawy, F. A. A., & Ali, I. T. (2026) · Iraqi Journal of Computer Communication Control and System Engineering · doi:10.33103/2617-3352.1524

Federated Learning: Healthcare, Security, Challenges, and ThreatsPreprint

Sumanth, N., Heggere, S. B., Prathiksha, E., Shetty, C. A., Nikhitha, H. R., & Soundarya, B. C. (2023) · Research Square · doi:10.21203/rs.3.rs-3223963/v1