A Secure Federated Learning Framework for 5G Networks

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

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

Geyer, R. C., Klein, T., & Nabi, M. (2017) · arXiv · doi:10.48550/arxiv.1712.07557 · Cited by 1,052

Federated Learning of Deep Networks using Model AveragingPreprint

McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 1,005

Reliable Federated Learning for Mobile Networks

Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900119 · Cited by 617

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

Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610 · cited 1×

GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy

Xu, C., Ren, J., Zhang, D., Zhang, Y., Qin, Z., & Ren, K. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2897874 · Cited by 214

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

Fan, K., Ren, Y., Wang, Y., Li, H., & Yang, Y. (2018) · IET Communications · doi:10.1049/iet-com.2017.0619 · Cited by 177

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

Kurtulmus, A. B., & Daniel, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10185 · Cited by 143

Hybrid NOMA for Latency Minimization in Wireless Federated Learning for 6G Networks

Kavitha, P., & Kavitha, K. (2023) · Radioengineering · doi:10.13164/re.2023.0594 · Cited by 3

A Dynamic Weighted Averaging Technique for Trust Assessment in Cloud Computing

Mehraj, S., & Banday, M. T. (2022) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.297099 · Cited by 3

Elite-CAM: An Elite Channel Allocation and Mapping for Policy Engine over Cognitive Radio Technology in 5G

Babu, C. R., Balakrishnan, A., Ramana, K., Singh, S., & Ra, I.-H. (2022) · Sensors · doi:10.3390/s22135011 · Cited by 3

The importance of 5G healthcare using blockchain technologies

Shrimali, B., & Surati, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90615-9.00007-4 · Cited by 3

Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning

Sikandar, H. S., Malik, S. U. R., Anjum, A., Khan, A., & Jeon, G. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.07.037 · Cited by 4

PC-SSL: Peer-Coordinated Sequential Split Learning for Intelligent Traffic Analysis in mmWave 5G Networks

Bedda, K., Fouda, M. M., & Fadlullah, Z. M. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10293872 · Cited by 3

Blockchain and Artificial Intelligence: Scientometric Analysis and Visualization

Adel, K., Elhakeem, A., & Marzouk, M. M. (2023) · IEEE Access · doi:10.1109/access.2023.3339752 · Cited by 9

Blockchain and Edge Computing Enabled Federated Learning Fault Diagnosis Framework

Shao, H., Yiming, X., Zhishan, M., Songyu, H., & Haizhou, Z. (2023) · Journal of Mechanical Engineering · doi:10.3901/jme.2023.21.283 · Cited by 3

Reinforcement Learning Based Friendly Jamming for Digital Twins Against Active Eavesdropping

Li, K., Ren, Y., Lin, Z., & Liang, X. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00050 · Cited by 3

Incentivized Federated Learning with Local Differential Privacy Using Permissioned Blockchains

Chaudhury, S. D., Reddy, L., Varun, M., Sengupta, T., Chakraborty, S., Sural, S., Vaidya, J. S., & Atluri, V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65172-4_19 · Cited by 3

Fedeval: Defending Against Lazybone Attack via Multi-dimension Evaluation in Federated Learning

Wang, H., Zhang, H., Wang, L., Xuan, S., & Zhang, Q. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3703631 · Cited by 3

TASS: Threshold Adaptor Signature Scheme for Holographic IoT Consumer Devices in Blockchain Enabled Federated Learning

Wang, Z., Qin, Z., Kumari, S., Feng, C., Alenazi, M. J. F., Wu, D., Chen, C., & Xiong, H. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3573156 · Cited by 3

How far should we go away from smart contract to smarter contractor? A systematic review

Ding, H., Li, Q., Wang, C., Ren, H., Li, J., Piao, X., Song, H., & Ji, Z. (2025) · Blockchain Research and Applications · doi:10.1016/j.bcra.2025.100402 · Cited by 3

Mobile Blockchain-Empowered Federated Learning: Current Situation And Further Prospect

Wibowo, D. S., & Fong, S. J. (2021) · International Conference on Blockchain Computing and Applications (BCCA) · doi:10.1109/bcca53669.2021.9656998 · Cited by 3

An implementation of an artifact for security in 5G networks using deep learning methods

Estrada, C. A., Fuertes, W., & Cruz, H. (2021) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v9i3.2197 · Cited by 4

Towards Heterogeneous Federated Learning: Analysis, Solutions, and Future Directions

Lin, Y., Long, Y., Zhou, Z., Pang, Y., & Yang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-99-9785-5_13 · Cited by 2

Mitigation of Membership Inference Attack by Knowledge Distillation on Federated Learning

Rei, U. E. D. A., Nakai, T., Yoshida, K., & Fujino, T. (2024) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2024cip0004 · Cited by 2

A systematic literature mapping on the security of 5G and vertical applications

Sobrinho, Á., dos Santos, M. V. P., Silva, A. B., Santos, D. F. S., & Gurjão, E. C. (2024) · Revista Brasileira de Computação Aplicada · doi:10.5335/rbca.v16i3.16005 · Cited by 2

Optimizing Privacy and Efficiency in Brain Tumor Classification through Advanced Non-IID Federated Deep Learning

Samsuzzaman, M., Sezan, R. H., Golder, A., Masud, M. A., & Bashir, G. M. M. (2024) · doi:10.1109/icict64387.2024.10839736 · Cited by 2

FORTRESS-FL: Byzantine-robust and privacy-preserving federated orchestration for next-generation networks

Dang, Q.-V., Vo, T.-B.-D., & Nguyen, N.-S.-A. (2026) · Array · doi:10.1016/j.array.2026.100680 · Cited by 2

Personalized Federated Learning System Based on Permissioned Blockchain

Yuan, B., & Qiu, W. (2021) · International Conference on Intelligent Computing, Automation and Systems (ICICAS) · doi:10.1109/icicas53977.2021.00026 · Cited by 2

A Blockchain-Enabled Decentralized Federated Learning System with Transparent and Open Incentive and Audit Contracts

Cai, Z., Chen, J., Diao, Z., & Xiang, H. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8101-4_18 · Cited by 2

Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution

Sagduyu, Y. E., Erpek, T., & Shi, Y. (2023) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom58377.2023.10356384 · Cited by 2

Civil engineering structure monitoring based on Integrated sensor fusion device with an optimized mathematical model

Ibrahim, A. K., Fouad, L., Hussien, N. A., Sherif, N. H., & Ali, A. (2023) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434244 · Cited by 2

Public Blockchain-Based Data Integrity Protection for Federated Learning in UAV Networks Using MAVLink Protocol

Khor, J. H., Sidorov, M., Law, S. Z., Tan, S. Y., & Woon, P. Y. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-9833-3_23 · Cited by 2

Robust Federated Learning Against Targeted Attackers Using Model Updates Correlation

Ranjan, P., Gupta, A., & Das, S. K. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-58923-2_4 · Cited by 3

Decentralization and Federated Approach for Personal Data Protection and Privacy Control

Singh, S., Verma, S. B., Gupta, B. K., & Agrawal, A. (2024) · Journal of Information assurance and security · doi:10.2478/ias-2024-0014 · Cited by 2

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

Semi-FLChain: Toward Secure and Efficient Semi-Supervised Federated Learning for IoT Using Blockchain

Qi, Y., & Liu, G. (2023) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics58376.2023.10235479 · Cited by 2

Communication Efficient Personalized Federated Learning via Hierarchical Clustering and Layer-wise Aggregation

Shuang, M., Zhang, Z., & Zhao, Y. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00037 · Cited by 2

Federated Learning for Internet of Medical Things

Tanwar, S., Verma, A., & Bhattacharya, P. (2023) · CRC Press eBooks · doi:10.1201/9781003303374 · Cited by 1

Applying software‐defined ambient intelligence techniques for cognitive and emotional awareness in the Internet of Things

Syed, S. A., Nag, I., Basu, S., & Sharma, D. K. (2023) · International Journal of Communication Systems · doi:10.1002/dac.5649 · Cited by 1

PriSecFedFR: Privacy-secure face recognition model training via federated learning and random projection

Peng, J., Sun, H., Yang, D., El‐Latif, A. A. A., & Rodrigues, J. J. P. C. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129383 · Cited by 1

Architectural patterns for blockchain-enabled federated learning: A systematic literature review

Wang, J., Shi, D., Li, S., Yan, J., Wang, Y., Mao, Q., Liu, Y., Zhu, L., & Zhang, H. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112695 · Cited by 1

Blockchain and Federated Learning(BFL) for Robust IoT Security: NONU

Shekhar, Bhardwaj, V. K., Essa, I. M., Gharban, H. A. J., Singh, R. K., & Kumar, V. (2025) · doi:10.1109/cises66934.2025.11264883 · Cited by 1

FedDHKD: A Lightweight Federated Learning Framework Based on Dynamic Hierarchical Knowledge Distillation for 5G IoT

Lv, J., Zhao, Q., Tang, H., Qiu, H., You, W., & Zhao, Y. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00342 · Cited by 1

An Explainable AI with Federated EfficientNet-B3 and Multi-Head Attention for Privacy-Preserving Lung Cancer Classification

Tabashoum, S., Roza, A. A., & Masud, M. A. (2025) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti69347.2025.11367506 · Cited by 1

A Novel Front-haul Bandwidth Compression Method for RAN Systems

Vankayala, S. K., Potnis, G., Shenoy, K. G., Yoon, S., & Kumar, S. (2020) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants50601.2020.9342832 · Cited by 1

An efficient data collection framework in the sky: An affine transformation approach based on Internet of unmanned aerial vehicles

Sun, B., Yu, M., Wu, Y., Qi, Y., Nie, X., & Xi, S. (2021) · IET Communications · doi:10.1049/cmu2.12110 · Cited by 1

A Blockchain-Integrated Divided-Block Sparse Matrix Transformation Differential Privacy Data Publishing Model

Hong, Y., Zhao, X., Zhu, H., & Li, H. (2021) · Security and Communication Networks · doi:10.1155/2021/2418539 · Cited by 1

Optimal Scheduling of Shared Vehicles Based on Improved Ant Colony Algorithm

Zhu, Z., Gao, Z., Peng, J., & Du, X. (2021) · IEEE International Conference on Data Science and Computer Application (ICDSCA) · doi:10.1109/icdsca53499.2021.9650102 · Cited by 1

COCO: Coherent Consensus Schema For Dynamic Spectrum Allocation For 5G

Babu, C. R., Ramana, K., Jeya, R., & Srinivasulu, A. (2022) · Complexity · doi:10.1155/2022/1388941 · Cited by 1

Blockchain-Based Secure and Efficient Federated Learning with Three-phase Consensus and Unknown Device Selection

Wang, J., Sun, H., & Xu, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19208-1_37 · Cited by 1

Increasing the Accuracy of Secure Model for Medical Data Sharing in the Internet of Things

Wu, J., Zhang, H., Li, G., & Yu, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19208-1_4 · Cited by 1

Integrated Sensor Fusion Device with an Optimized Mathematical Model to Monitor Civil Engineering Structures

Tan, J., & Li, D. (2023) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20230429000587 · Cited by 1

Inverse Feasibility in Over-the-Air Federated Learning

Piotrowski, T., Ismayilov, R., Frey, M., & Cavalcante, R. L. G. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3400916 · Cited by 1

Blockchain-Enabled Privacy-Preserving Federated Learning Algorithms in Edge Computing

Zhao, J. (2024) · International Conference on Computer Science and Blockchain (CCSB) · doi:10.1109/ccsb63463.2024.10735574 · Cited by 1

TBFL: blockchain-enabled trusted byzantine-robust federated learning framework for photovoltaic power generation forecasting

Zhang, Y., Wang, L., Luo, Y., Zhang, K., Yu, L., & Chen, K. (2025) · The Computer Journal · doi:10.1093/comjnl/bxae141 · Cited by 1

Computer Vision Operating System of Bank Economic Management Security under 5G Wireless Communication Technology

Guo, J., & Xiaoyan, W. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/6233870 · Cited by 1

Fog-Assisted Smart Healthcare Prediction System for Diabetics Patients

Tripathy, S. S., Panda, S. B., Pati, A., Rath, M., Tripathy, N., & Sahu, P. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-2271-0_8 · Cited by 1

Low Delay Transmission Model of Internet of Things Based on Data Spectrum

Liu, X., Liu, J., & Gong, Y. (2022) · Journal of Interconnection Networks · doi:10.1142/s0219265921430349

Research Model of Optimal Catalytic Combination Based on Machine Learning

Peng, J., Zhu, Z., & Tian, Y. (2021) · International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) · doi:10.1145/3495018.3501166

5G Wireless Multimedia-Assisted Marketing Strategy Establishment and Effect Evaluation

Fu, T., & Zhang, B. (2022) · Journal of Sensors · doi:10.1155/2022/6485602

Blockchain Driven Edge Intelligence

Wang, X., Qiu, C., Ren, X., Xiong, Z., Leung, V. C. M., & Niyato, D. T. (2022) · Wireless networks · doi:10.1007/978-3-031-10186-1_4

Research on the Framework of Confederate Modelling System

Liu, Y., An, B., & Zhou, Q. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0416-7_77

Cooperative Game of Energy‐Constrained Agents in Wireless Communication Systems through Reinforcement Learning

Guo, L., Wang, J., Huang, D. K., & Feng, S. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/9973580

Trust Score-based Federated Learning to Defend against Data Poisoning Attacks

Ou, S., Jeong, S., & Kim, K. (2023) · Journal of Digital Contents Society · doi:10.9728/dcs.2023.24.6.1317

Evaluation of Membership Inference Attack Against Federated Learning With Differential Privacy on Edge Devices

Ueda, R., Nakai, T., Yoshida, K., & Fujino, T. (2023) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce59613.2023.10315549

A Blockchain Based Trustworthy Space in Cross-Domain Synergy

Sun, L., Jia, X., Liu, J., Song, Y., Xie, Y., & Xue, C. (2023) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain60715.2023.00042

A Brief Overview of Federated Learning - A New Perspective on Data Privacy

Lipovanu, I., Pascal, C. M., & Caruntu, C. F. (2022) · Buletinul Institutului Politehnic din Iaşi. Secţia Electrotehnică. Energetică. Electronică · doi:10.2478/bipie-2022-0019

Intelligent Maintenance System for Wind Turbine Based on Deep Belief Federated Learning

Hu, X., Tao, J., Yang, D., Chen, H., Li, Z., & Yin, S. (2023) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys60770.2023.00152

Robust Federated Learning for Edge Intelligence

Yu, D., Zhang, X., He, H., Chen, S., Qiao, J., Wang, Y., & Cheng, X. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-58923-2_11

Intra-Slice Aggregated Defender - ISAD: Federated Learning Based Framework to Enhance 5G Intra-Slicing Security

Baz, N., & Rahim-Amoud, R. (2024) · Arab ICT Conference (AICTC) · doi:10.1109/aictc58357.2024.10735027

A Secure Federated Load Forecasting Scheme for Power Distribution in Smart Grids

Yang, Y., Gu, Z., Su, P., & Lu, M. (2025) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs66275.2025.00107

DBFFL - Defending Against Dynamic Poisoning Attacks in Federated Learning in 5G and Beyond Systems

Shashidhar, R., Siriwardhana, Y., Misra, M., & Liyanage, M. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161138

Federated Learning Over Blockchain for Collaborative AI Training

Laurent, D. I. (2025) · Scientific Journal of Artificial Intelligence and Blockchain Technologies · doi:10.63345/sjaibt.v2.i2.303

Enhanced Security and Privacy Framework for Federated Learning in Beyond 5G IoT Networks

Jalali, N. A., Hongsong, C., Zahin, F. A., & Ashiru, A. (2025) · Wireless Personal Communications · doi:10.1007/s11277-025-11841-0

Sybil-aware adaptive defence framework for robust federated learning

Khedekar, D., Mahapatra, T., & Rajput, A. S. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102157

Comparative Analysis of Privacy-Preserving Techniques in Pneumonia Detection

Agrawal, A., Sharma, A., Kesarwani, P., & Chawla, I. (2025) · International Conference on Contemporary Computing · doi:10.1109/ic366947.2025.11290390

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations

Shao, Y., Li, J., Shi, L. X., Wei, K., Ding, M., Li, Q., Li, Z., Chen, W., & Jin, S. (2026) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2025.3647650

Clustering and Aggregation for Wireless Federated Learning Under Label-Flipping Attacks

Ads, M., Zhagypar, R., Kouzayha, N., ElSawy, H., Al-Naffouri, T. Y., & Hassanein, H. S. (2026) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2026.3654024

M3D-HFL: A Dual-Metric Approach for Malicious Model Detection in Hierarchical Federated Learning

Fu, K., Gu, Z., Ying, Y., Zhang, J., & Wang, C. (2025) · International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI) · doi:10.1109/iccbdai66607.2025.11387981

FedSSV: Client Contribution for Secure Federated Learning Aggregation Using Scaled Shapley Values

Sani, S. D., Olapojoye, R., Salman, T., Ji, T., & Teixeira, M. A. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3674950 · Cited by 1

Security architectures for network slice management for 5G and beyond

Saad, S. B. (2023) · doi:10.70675/3d5f78d9z253az4af6zad99zee68fefb4e95

Towards a secure data sharing management approach for IoT environments

Laamech, N. (2024) · theses.fr (ABES) · doi:10.70675/6f8c36a7z282fz4babz944cz0316a1640061

FedBlockSiamese: Federated Learning with Blockchain for Medical Images with Siamese Network

Anusuya, R., & Karthika Renuka, D. (2026) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967261438599

An Explainable Federated EfficientNet–Transformer Model for Lung Cancer Classification from Histopathological CT Images

Tabashoum, S., Roza, A. A., Masud, M. A., Golder, A., & Sezan, R. H. (2025) · IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON) · doi:10.1109/spicscon69221.2025.11504440

Cross-Jurisdictional Smart City Traffic Governance: A Unified Framework with Graph Attention Networks and Decision ProvenancePreprint

Khan, I., Butt, U., Rashid, A., & Naqvi, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6833807

SecureDFL: A Secure Distributed Federated Learning Framework Against Poisoning Attacks

Javadpour, A., Ja’fari, F., Taleb, T., Türkmen, F., Benzaïd, C., & Shojafar, M. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3697583 · Cited by 1

Federated Learning with LLM Governance for Healthcare Cybersecurity

Reddy, B. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-37241

A Secure Data Collaboration Framework Using Differential Partial AI and Federated Learning: Architecture, Simulation, and Performance Evaluation

Haidar, S. W., Mishra, A. D., Chaubey, S. K., Akhtar, M. M., Mehmood, Z., & Khan, N. A. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.05.164

Federated continual learning with joint diffusion-based generative replay

Li, Y., Chen, Y., Ma, Y., Yin, Y., & Hu, S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115928

Privacy-Preserving Federated AI on Blockchain for Secure and Compliant Banking Data Management

Lakkadi, V. R., Donthi, P. R., Kandula, S., & Kumar, M. (2026) · IEEE International Conference on AI and Data Analytics (ICAD) · doi:10.1109/icad69378.2026.11608846

Toward Fair Federated Edge Learning Through Prototype-Guided Distributed Adversarial Networks

Wei, K., Yuan, X., Hong, Z., Zhang, J., Guo, J., Ding, M., Li, J., & Guo, S. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3716806

СУЧАСНІ КІБЕРЗАГРОЗИ ТЕЛЕКОМУНІКАЦІЙНОЇ ІНФРАСТРУКТУРИ: АНАЛІЗ І ОЦІНКА

Чухов, В., & Морозов, Д. (2026) · doi:10.62731/mcnd-19.06.2026.006

Causal-CISP: Hybrid causality and adaptive security for robust federated learning under adversarial threats

Dubey, P., & Kumar, M. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116817

SATP-FL: Lightweight Secure Aggregation via Trajectory Prediction in Vehicular Federated LearningPreprint

Xiong, P., & Ding, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7388938

DPPF: a decentralized privacy-preserved framework using blockchain and federated learning for IoT driven smart cities

Mazid, A., Kirmani, S., Pawar, V., Yadav, A. S., & Abid, M. (2026) · Wireless Networks · doi:10.1007/s11276-026-04229-w

Securing FL-Enabled NWDAF in Private 5G Networks Against Hybrid Attacks

Xiong, R., Xiong, M., Ren, Y. C., Ren, W., & Parr, G. P. (2026) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2026.3735240

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

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