A Secure Federated Learning Framework for 5G Networks

Communication-Efficient Learning of Deep Networks from Decentralized Data

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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

Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754

Differentially Private Federated Learning: A Client Level Perspective

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Federated Learning of Deep Networks using Model AveragingPreprint

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Reliable Federated Learning for Mobile Networks

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DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

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GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy

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Blockchain-based efficient privacy preserving and data sharing scheme of content-centric network in 5G

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Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum BlockchainPreprint

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Membership Inference Attacks on Machine Learning: A Survey

Hu, H., Salcic, Z. A., Sun, L., Dobbie, G., Yu, P. S., & Zhang, X. (2022) · ACM Computing Surveys · doi:10.1145/3523273 · Cited by 662

Federated learning for 6G communications: Challenges, methods, and future directions

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Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT

Jia, B., Zhang, X., Liu, J., Zhang, Y., Huang, K., & Liang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3085960 · Cited by 358

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342

Asynchronous federated learning on heterogeneous devices: A survey

Xu, C., Qu, Y., Xiang, Y., & Gao, L. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100595 · Cited by 406

Privacy-preserving blockchain-based federated learning for traffic flow prediction

Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.003 · Cited by 339

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

An adaptive federated learning scheme with differential privacy preserving

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Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

Zhang, P., Wang, C., Jiang, C., & Han, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3064351 · Cited by 238

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

Li, J., Shao, Y., Wei, K., Ding, M., Ma, C., Shi, L. X., Han, Z., & Poor, H. V. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3138848 · Cited by 244

Poisoning attacks in federated learning: A survey

Xia, G., Chen, J., Yu, C., & Ma, J. (2023) · IEEE Access · doi:10.1109/access.2023.3238823 · Cited by 212

6G-Enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions

Chang, L., Zhang, Z., Li, P., Xi, S., Guo, W., Shen, Y., Xiong, Z., Kang, J., Niyato, D. T., Qiao, X., et al. (2022) · Journal of Communications and Information Networks · doi:10.23919/jcin.2022.9815195 · Cited by 204

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey

Li, D., Han, D., Weng, T., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K. (2021) · Soft Computing · doi:10.1007/s00500-021-06496-5 · Cited by 221

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

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

Ihnaini, B., Khan, M. A., Khan, T. A., Abbas, S., Daoud, M. S., Ahmad, M., & Khan, M. A. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4243700 · Cited by 148

Privacy-Preserving Aggregation in Federated Learning: A Survey

Liu, Z., Guo, J., Yang, W., Fan, J., Lam, K., & Zhao, J. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3190835 · Cited by 163

Artificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges

Alhammadi, A., Shayea, I. A. M., El‐Saleh, A. A., Azmi, M. H., Ismail, Z. H., Kouhalvandi, L., & Saad, S. A. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/8845070 · Cited by 168

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

Ali, R., Zikria, Y. B., Bashir, A. K., Garg, S., & Kim, H. S. (2021) · IEEE Access · doi:10.1109/access.2021.3073806 · Cited by 139

A Blockchain‐Based Federated Learning Method for Smart Healthcare

Chang, Y., Fang, C., & Sun, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4376418 · Cited by 119

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan, M. M., Mohjazi, L., Bariah, L., Centeno, A., Zoha, A., Arshad, K., Assaleh, K. T., Muhaidat, S., Debbah, M. A., & Imran, M. A. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3251404 · Cited by 159

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Zhang, L., Shen, B., Barnawi, A., Xi, S., Kumar, N., & Wu, Y. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10144-6 · Cited by 112

On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives

Rahman, A., Hasan, K., Kundu, D., Islam, M. J., Debnath, T., Band, S. S., & Kumar, N. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.08.004 · Cited by 115

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

Reputation-Based Federated Learning for Secure Wireless Networks

Song, Z., Sun, H., Yang, H. H., Wang, X., Zhang, Y., & Quek, T. Q. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079104 · Cited by 107

Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics

Ding, Y., Yang, Z., Pham, Q., Hu, Y., Zhang, Z., & Shikh‐Bahaei, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3341307 · Cited by 123

BCE-FL: A Secure and Privacy-Preserving Federated Learning System for Device Fault Diagnosis Under Non-IID Condition in IIoT

Xiao, Y., Shao, H., Lin, J., Huo, Z., & Liu, B. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3340745 · Cited by 101

Federated learning for 6G-enabled secure communication systems: a comprehensive survey

Sirohi, D., Kumar, N., Rana, P. S., Tanwar, S., Iqbal, R., & Hijji, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10417-3 · Cited by 100

Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

Yaacoub, J.-P. A., Noura, H., & Salman, O. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.001 · Cited by 106

Efficient Workload Allocation and User-Centric Utility Maximization for Task Scheduling in Collaborative Vehicular Edge Computing

Huang, X., Yu, R., Ye, D., Shu, L., & Xie, S. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3064426 · Cited by 86

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

A comprehensive review on blockchains for Internet of Vehicles: Challenges and directions

Hildebrand, B., Baza, M., Salman, T., Tabassum, S., Konatham, B., Amsaad, F., & Razaque, A. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100547 · Cited by 96

Securing Critical IoT Infrastructures With Blockchain-Supported Federated Learning

Otoum, S., Ridhawi, I. A., & Mouftah, H. T. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3088056 · Cited by 83

Integrating Edge Intelligence and Blockchain: What, Why, and How

Wang, X., Ren, X., Qiu, C., Xiong, Z., Yao, H., & Leung, V. C. M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3189962 · Cited by 83

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

Adapting security and decentralized knowledge enhancement in federated learning using blockchain technology: literature review

Orabi, M. M., Emam, O. E., & Fahmy, H. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01099-5 · Cited by 77

Trustworthy Privacy-Preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 With Blockchain

Stephanie, V., Khalil, I., Atiquzzaman, M., & Yi, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3214998 · Cited by 82

Machine Learning Threatens 5G Security

Suomalainen, J., Juhola, A., Shahabuddin, S., Mämmelä, A., & Ahmad, I. (2020) · IEEE Access · doi:10.1109/access.2020.3031966 · Cited by 80

APDPFL: Anti-Poisoning Attack Decentralized Privacy Enhanced Federated Learning Scheme for Flight Operation Data Sharing

Li, X., Zhao, H., Xu, J., Zhu, G., & Deng, W. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3479149 · Cited by 74

Blockchain-Based Federated Learning: A Survey and New Perspectives

Ning, W., Zhu, Y., Song, C., Li, H., Zhu, L., Xie, J., Chen, T., Xu, T., Xi, X., & Gao, J. (2024) · Applied Sciences · doi:10.3390/app14209459 · Cited by 78

A Blockchain-Based Federated Learning Scheme for Data Sharing in Industrial Internet of Things

Xu, G., Zhou, Z., Dong, J., Zhang, L., & Song, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3298196 · Cited by 68

Integration of federated learning with IoT for smart cities applications, challenges, and solutions

Ghadi, Y. Y., Mazhar, T., Shah, S. F. A., Haq, I., Ahmad, W., Ouahada, K., & Hamam, H. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1657 · Cited by 71

Decentralized federated learning based on blockchain: concepts, framework, and challenges

Zhang, H., Jiang, S., & Xuan, S. (2024) · Computer Communications · doi:10.1016/j.comcom.2023.12.042 · Cited by 65

Toward Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward

Sharma, G. P., Patel, D., Sachs, J., Andrade, M. D., Farkas, J., Harmatos, J., Varga, B., Bernhard, H.-P., Muzaffar, R., Ahmed, M., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3316605 · Cited by 64

Exploring Targeted and Stealthy False Data Injection Attacks via Adversarial Machine Learning

Tian, J., Wang, B., Li, J., Wang, Z., Ma, B., & Özay, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3147040 · Cited by 63

Blockchain-Based Federated Learning With Enhanced Privacy and Security Using Homomorphic Encryption and Reputation

Yang, R., Zhao, T., Yu, F. R., Li, M., Zhang, D., & Zhao, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3379395 · Cited by 71

Robust Semisupervised Federated Learning for Images Automatic Recognition in Internet of Drones

Zhang, Z., Ma, S., Yang, Z., Xiong, Z., Kang, J., Wu, Y., Zhang, K., & Niyato, D. T. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3151945 · Cited by 61

Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

Zhu, C., Xiao, Z., Ren, J., & Qin, T. (2022) · IEEE Access · doi:10.1109/access.2022.3174865 · Cited by 60

Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering

He, Z., Yin, J., Wang, Y., Gui, G., Adebisi, B., Ohtsuki, T. O., Gacanin, H., & Sari, H. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3101239 · Cited by 57

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Du, Y., Wang, Z., & Leung, V. C. M. (2021) · Future Internet · doi:10.3390/fi13020048 · Cited by 55

A Practical Cross-Device Federated Learning Framework over 5G Networks

Yang, W., Wang, N., Guan, Z., Wu, L., Du, X., & Guizani, M. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.005.2100435 · Cited by 55

Federated Reinforcement Learning for Collective Navigation of Robotic Swarms

Na, S., Rouček, T., Ulrich, J., Pikman, J., Krajník, T., Lennox, B., & Arvin, F. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3239815 · Cited by 60

Federated Zero Trust Architecture using Artificial Intelligence

Hussain, M., Pal, S., Jadidi, Z., Foo, E., & Kanhere, S. S. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.001.2300405 · Cited by 53

A survey on blockchain-enabled federated learning and its prospects with digital twin

Liu, K., Yan, Z., Liang, X., Kantola, R., & Hu, C. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.08.001 · Cited by 52

Federated learning for big data: A survey on opportunities, applications, and future directions

Gadekallu, T. R., Pham, Q., Huynh‐The, T., Feng, H., Fang, K., Pandya, S., Liyanage, M., Wang, W. W., & Nguyen, T. T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113614 · Cited by 86

Energy-Efficient Relay-Based Void Hole Prevention and Repair in Clustered Multi-AUV Underwater Wireless Sensor Network

Chaaf, A., Muthanna, M. S. A., Muthanna, A., Alhelaly, S., Elgendy, I. A., Iliyasu, A. M., & El‐Latif, A. A. A. (2021) · Security and Communication Networks · doi:10.1155/2021/9969605 · Cited by 48

Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO

Vu, T. T., Ngo, D. T., Ngo, H. Q., Dao, M. N., Tran, N. H. K., & Middleton, R. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3183295 · Cited by 49

Communication-Efficient Federated Learning

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

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

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

Defenses to Membership Inference Attacks: A Survey

Hu, L. F., Yan, A., Yan, H., Li, J., Huang, T., Zhang, Y., Dong, C., & Yang, C. (2023) · ACM Computing Surveys · doi:10.1145/3620667 · Cited by 53

Privacy-aware detection framework to mitigate new-age phishing attacks

Bhardwaj, A., Al-Turjman, F. M., Sapra, V., Kumar, M., & Stephan, T. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107546 · Cited by 46

Heterogeneous Data-Aware Federated Learning for Intrusion Detection Systems via Meta-Sampling in Artificial Intelligence of Things

Han, W., Peng, J., Jia-hua, Y., Kang, J., Lu, J., & Niyato, D. T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3337755 · Cited by 49

Towards Communication-Efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

Liu, Y., Zhao, R., Kang, J., Yassine, A., Niyato, D. T., & Peng, J. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3453169 · Cited by 49

Deep data plane programming and AI for zero-trust self-driven networking in beyond 5G

Hireche, O., Benzaïd, C., & Taleb, T. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108668 · Cited by 45

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

Federated Learning in Massive MIMO 6G Networks: Convergence Analysis and Communication-Efficient Design

Mu, Y., Garg, N., & Ratnarajah, T. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3196463 · Cited by 46

Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey

Wang, Z., Kang, Q., Zhang, X., & Hu, Q. (2022) · 2022 IEEE Wireless Communications and Networking Conference (WCNC) · doi:10.1109/wcnc51071.2022.9771619 · Cited by 41

Toward Securing Federated Learning Against Poisoning Attacks in Zero Touch B5G Networks

Saad, S. B., Brik, B., & Ksentini, A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3278838 · Cited by 41

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Yu, G., Wang, X., Sun, C., Wang, Q., Yu, P., Ni, W., & Liu, R. P. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3329249 · Cited by 41

Misbehavior Detection in Vehicular Ad Hoc Networks Based on Privacy-Preserving Federated Learning and Blockchain

Lv, P., Xie, L., Xu, J. W., Wu, X., & Li, T. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3220779 · Cited by 43

Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired ConsensusPreprint

Chen, H., Asif, S. A., Park, J., Shen, C.-C., & Bennis, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03300 · Cited by 54

PriMonitor: An adaptive tuning privacy-preserving approach for multimodal emotion detection

Yin, L., Lin, S., Sun, Z., Wang, S., Li, R., & He, Y. (2024) · World Wide Web · doi:10.1007/s11280-024-01246-7 · Cited by 40

A review on client-server attacks and defenses in federated learning

Sharma, A., & Marchang, N. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103801 · Cited by 46

A Survey on Physical Layer Security of Ultra/Hyper Reliable Low Latency Communication in 5G and 6G Networks: Recent Advancements, Challenges, and Future Directions

Pradhan, A., Das, S., Piran, M. J., & Han, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3429285 · Cited by 51

Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things

Tang, Y., Liang, Y., Liu, Y., Zhang, J., Ni, L., & Qi, L. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.019 · Cited by 39

A survey of secure semantic communications

Meng, R., Gao, S., Fan, D., Gao, H., Wang, Y., Xu, X., Wang, B., Lv, S., Zhang, Z., Sun, M., et al. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104181 · Cited by 52

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Kamišalić, A., Kramberger, R., & Fister, I. (2021) · Applied Sciences · doi:10.3390/app11177987 · Cited by 43

Survey on Federated-Learning Approaches in Distributed Environment

Gupta, R., & Alam, T. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-09624-y · Cited by 43

A blockchain-based framework for federated learning with privacy preservation in power load forecasting

Mao, Q., Wang, L., Yu, L., Han, L., Wang, Z., & Chen, K. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111338 · Cited by 40

Secure and Trusted Collaborative Learning Based on Blockchain for Artificial Intelligence of Things

Tang, X., Zhu, L., Shen, M., Peng, J., Kang, J., Niyato, D. T., & El‐Latif, A. A. A. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.003.2100598 · Cited by 38

Quantum Federated Learning: Remarks and Challenges

Larasati, H. T., Firdaus, M., & Kim, H. (2022) · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · doi:10.1109/cscloud-edgecom54986.2022.00010 · Cited by 37

Asynchronous Federated Learning System Based on Permissioned Blockchains

Wang, R., & Tsai, W. (2022) · Sensors · doi:10.3390/s22041672 · Cited by 37

Collaborative Federated Learning for 6G With a Deep Reinforcement Learning-Based Controlling Mechanism: A DDoS Attack Detection Scenario

Kianpisheh, S., & Taleb, T. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3387987 · Cited by 36

Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

Asqah, M. A., & Moulahi, T. (2023) · Future Internet · doi:10.3390/fi15060203 · Cited by 41

Trust Management of Tiny Federated Learning in Internet of Unmanned Aerial Vehicles

Zheng, J., Xu, J., Du, H., Niyato, D. T., Kang, J., Nie, J., & Wang, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3363443 · Cited by 35

FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things

Qu, Y., Xu, C., Gao, L., Xiang, Y., & Yu, S. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2100173 · Cited by 35

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Almutairi, M. S. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/6855435 · Cited by 36

Lattice-based public key searchable encryption with fine-grained access control for edge computing

Wang, P., Chen, B., Xiang, T., & Wang, Z. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.012 · Cited by 33

Survey on Mobile Edge-Cloud Computing: A Taxonomy on Computation offloading Approaches

Elgendy, I. A., & Yadav, R. (2021) · Studies in big data · doi:10.1007/978-3-030-85428-7_6 · Cited by 32

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline

Al-lahham, A., Zaheer, M. Z., Tastan, N., & Nandakumar, K. (2024) · 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/cvpr52733.2024.01180 · Cited by 37

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

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

Edge Intelligence for Intelligent Transport Systems: Approaches, challenges, and future directions

Ghasemi, A., Keshavarzi, A., Abdelmoniem, A. M., Nejati, O. R., & Derikvand, T. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127273 · Cited by 31

Blockchain Assisted Federated Learning for Enabling Network Edge Intelligence

Wang, Y., Zhou, J., Feng, G., Niu, X., & Qin, S. (2022) · IEEE Network · doi:10.1109/mnet.115.2200014 · Cited by 31

Privacy preserving and secure robust federated learning: A survey

Han, Q., Lu, S., Wang, W., Qu, H., Li, J., & Gao, Y. (2024) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.8084 · Cited by 29

Resilience and Failure Analysis in Next-Generation Communication Networks: A Contemporary Survey

Bi, S., Yuan, X., Hu, S., Li, K., Ni, W., Hossain, E., & Wang, X. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3620950 · Cited by 31

DDoS Attack Detection in Vehicular Ad-Hoc Network (VANET) for 5G Networks

Gaurav, A., Gupta, B. B., García‐Peñalvo, F. J., Nedjah, N., & Psannis, K. E. (2021) · Studies in big data · doi:10.1007/978-3-030-85428-7_11 · Cited by 29

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

BFKD: Blockchain-Based Federated Knowledge Distillation for Aviation Internet of Things

Deng, W., Li, X., Xu, J., Li, W., Zhu, G., & Zhao, H. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3474710 · Cited by 28

Blockchain-Based Clustered Federated Learning for Non-Intrusive Load Monitoring

Wang, T., & Dong, Z. Y. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3326194 · Cited by 27

A 5G-Based eHealth Monitoring and Emergency Response System: Experience and Lessons Learned

Antevski, K., Girletti, L., Bernardos, C. J., de la Oliva, A., Baranda, J., & Mangues‐Bafalluy, J. (2021) · IEEE Access · doi:10.1109/access.2021.3114593 · Cited by 26

An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection

Kholidy, H. A., & Kamaludeen, R. (2022) · IEEE Future Networks World Forum (FNWF) · doi:10.1109/fnwf55208.2022.00033 · Cited by 14

BSFL: A blockchain-oriented secure federated learning scheme for 5G

Han, G., Ma, W., Zhang, Y., Liu, Y., & Liu, S. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.103983 · Cited by 3

Developing Robust Federated Learning-Based Anomaly Detection Against Poisoning Attacks in 5G Networks

., A., & Singh, A. (2026) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/26apr2363

Standing Firm in 5G: A Single-Round, Dropout-Resilient Secure Aggregation for Federated Learning

Zhang, Y., Behnia, R., Karim, I., Yavuz, A. A., & Bertino, E. (2025) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3734477.3734719 · Cited by 2

Robust and Resilient Federated Learning for Securing Future Networks

Siriwardhana, Y., Porambage, P., Liyanage, M., & Ylianttila, M. (2022) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit54941.2022.9815812 · Cited by 5