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

Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

Nwogbaga, N. E., Latip, R. B., Affendey, L. S., & Rahiman, A. R. A. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00288-4 · Cited by 26

Federated-Learning-Aided Next-Generation Edge Networks for Intelligent Services

Hazra, A., Adhikari, M., Nandy, S., Doulani, K., & Menon, V. G. (2022) · IEEE Network · doi:10.1109/mnet.007.2100549 · Cited by 26

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3055679 · Cited by 25

A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

Ahmed, U., Srivastava, G., & Lin, J. C. (2021) · Journal of Network and Systems Management · doi:10.1007/s10922-021-09609-5 · Cited by 26

Affordable federated edge learning framework via efficient Shapley value estimation

Dong, L., Liu, Z., Zhang, K., Yassine, A., & Hossain, M. S. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.05.007 · Cited by 25

Intrinsic Security and Self-Adaptive Cooperative Protection Enabling Cloud Native Network Slicing

Wu, Q., Wu, C., Yan, X., & Cheng, Q. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3071774 · Cited by 23

Bilateral Privacy Protection Scheme Based on Adaptive Location Generalization and Grouping Aggregation in Mobile Crowdsourcing

Sun, X., Wang, Y., Duan, P., Zia, Q., Wang, W., & Cai, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3358799 · Cited by 22

AI management platform for privacy-preserving indoor air quality control: Review and future directions

Quang, T. V., Doan, D. T., Ngarambe, J., GhaffarianHoseini, A., Ghaffarianhoseini, A., & Zhang, T. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111712 · Cited by 25

Stereo Attention Cross-Decoupling Fusion-Guided Federated Neural Learning for Hyperspectral Image Classification

Cai, W., Gao, M., Ding, Y., Ning, X., Bai, X. P., & Qian, P. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3320044 · Cited by 22

Blockchain-Based Federated Learning: A Systematic Survey

Huang, J., Kong, L., Chen, G., Xiang, Q., Chen, X., & Liu, X. (2022) · IEEE Network · doi:10.1109/mnet.129.2200346 · Cited by 21

BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Peng, Y., Chen, Z., Chen, Z., Ou, W., Han, W., & Ma, J. (2021) · Mobile Information Systems · doi:10.1155/2021/6633332 · Cited by 20

Bridging Research Gaps in Industry 5.0: Synergizing Federated Learning, Collaborative Robotics, and Autonomous Systems for Enhanced Operational Efficiency and Sustainability

Zia, A., & Haleem, M. (2025) · IEEE Access · doi:10.1109/access.2025.3541822 · Cited by 19

Advancing Federated Learning: A Systematic Literature Review of Methods, Challenges, and Applications

Sana, T. Z., Abdulla, S., Nag, A., Das, A., Hassan, M. M., Fiza, Z. Z., Karim, A., & Kabir, S. R. R. (2025) · IEEE Access · doi:10.1109/access.2025.3605165 · Cited by 25

The Block Chain Technology to Protect Data Access Using Intelligent Contracts Mechanism Security Framework for 5g Networks

Kapula, P. R., Jeslin, J. G., Hosamani, G., Vats, P., Shelke, C. J., & Shukla, S. K. (2022) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite53722.2022.9823471 · Cited by 19

A Secure Federated Learning Framework using Blockchain and Differential Privacy

Firdaus, M., Larasati, H. T., & Rhee, K.-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-edgecom54986.2022.00013 · Cited by 21

Blockchain‐Based Federated Learning Methodologies in Smart Environments for Drone Technology

Chalapathi, M. M. V., Sreenivasulu, K., Jeya, R., Faheem, M., Mohana, R. M., Khan, A. A., & Ramana, K. (2025) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.70025 · Cited by 18

Exploring SVM for Federated Machine Learning Applications

Nair, D. G., Narayana, C. V. A., Reddy, K. J., & Nair, J. J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1018-0_25 · Cited by 19

Federated learning based on dynamic hierarchical game incentives in Industrial Internet of Things

Tang, Y., Ni, L., Li, J., Zhang, J., & Liang, Y. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103214 · Cited by 19

Intelligent toll collection system for moving vehicles in India

Chauhan, R. K., & Chauhan, K. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200099 · Cited by 16

How to Launch Jamming Attacks on Federated Learning in NextG Wireless Networks

Shi, Y., & Sagduyu, Y. E. (2022) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps56602.2022.10008669 · Cited by 16

When Two-Layer Federated Learning and Mean-Field Game Meet 5G and Beyond Security: Cooperative Defense Systems for 5G and Beyond Network Slicing

Sedjelmaci, H., & Boualouache, A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3294568 · Cited by 16

GradDiff: Gradient-based membership inference attacks against federated distillation with differential comparison

Wang, X., Wu, L., & Guan, Z. (2023) · Information Sciences · doi:10.1016/j.ins.2023.120068 · Cited by 15

Towards blockchain-enabled decentralized and secure federated learning

Ma, X., Xu, D., & Wolter, K. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120368 · Cited by 15

NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks

Babbar, H., Rani, S., & Boulila, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-61298-7 · Cited by 15

Blockchain for Securing Federated Learning Systems: Enhancing Privacy and Trust

Vashishth, T. K., Sharma, V., Kumar, B., Sharma, K. K., Chaudhary, S., & Panwar, R. (2024) · doi:10.1002/9781394219230.ch15 · Cited by 15

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

Firdaus, M., Larasati, H. T., & Rhee, K.-H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039487 · Cited by 16

Advancing Medical Recommendations With Federated Learning on Decentralized Data: A Roadmap for Implementation

Kumari, R., Kumar, S., Gupta, S., Cengiz, K., & Ivković, N. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3334159 · Cited by 14

Revolutionizing Emergency Response: The Transformative Power of Smart Wearables Through Blockchain, Federated Learning, and Beyond 5G/6G Services

Ganesan, P., & Jagatheesaperumal, S. K. (2023) · IT Professional · doi:10.1109/mitp.2023.3323714 · Cited by 14

SoK: Federated Learning based Network Intrusion Detection in 5G: Context, State of the Art and Challenges

Chennoufi, S., Blanc, G., Jmila, H., & Kiennert, C. (2024) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3664476.3664500 · Cited by 14

Integration of Federated Learning and AI-Generated Content: A Survey of Overview, Opportunities, Challenges, and Solutions

Liu, Y., Yin, J., Zhang, W., An, C., Xia, Y., & Zhang, H. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3523350 · Cited by 17

Energy-Efficient Wireless Resource Allocation for Heterogeneous Federated Multitask Networks Based on Evolutionary Learning

Jiang, B., Cai, L., Yue, G., Luo, F., Li, S., & Wang, J. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3538096 · Cited by 14

Enhancement of Energy Efficiency and Network Lifetime Using Modified MPCT Algorithm in Wireless Sensor Networks

Satyanarayana, P., Mahalakshmi, T., Rao, P. R. K., Sheeba, A., Ravi, J., & Rao, J. N. (2022) · Journal of Interconnection Networks · doi:10.1142/s0219265921440126 · Cited by 14

Blockchain-Based Federated Learning System: A Survey on Design Choices

Oktian, Y. E., & Lee, S.-G. (2023) · Sensors · doi:10.3390/s23125658 · Cited by 14

Deep federated learning: a systematic review of methods, applications, and challenges

Cooray, L., Sendanayake, J., Vithanaarachchi, P., & Priyadarshana, Y. H. P. P. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1617597 · Cited by 16

Implementing Privacy-Preserving and Collaborative Industrial Artificial Intelligence

Peres, R. S., Manta-Costa, A., & Barata, J. (2023) · IEEE Access · doi:10.1109/access.2023.3296143 · Cited by 13

Federated learning incentivize with privacy-preserving for IoT in edge computing in the context of B5G

Jalali, N. A., & Hongsong, C. (2024) · Cluster Computing · doi:10.1007/s10586-024-04788-7 · Cited by 14

Blockchain Empowered Federated Learning for Distributed Network Security Behaviour Knowledge Base in 6G

Li, K., Zhou, H., Tu, Z., Liu, F., & Zhang, H. (2022) · Security and Communication Networks · doi:10.1155/2022/4233238 · Cited by 14

Dynamic Pricing for Client Recruitment in Federated Learning

Wang, X., Zheng, S., & Duan, L. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3312208 · Cited by 13

Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks

Sagduyu, Y. E. (2022) · IEEE Conference on Communications and Network Security · doi:10.1109/cns56114.2022.9947274 · Cited by 13

Method and application of information sharing throughout the emergency rescue process based on 5G and AR wearable devices

Wang, M., Ji, H., Jia, M., Sun, Z., Gu, J., & Ren, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-33610-4 · Cited by 13

A blockchain-based privacy-preserving 5G network slicing service level agreement audit scheme

Xiao, K., Geng, Z., He, Y., Xu, G., Wang, C., & Tian, Y. (2021) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-021-02037-8 · Cited by 13

LATEST ADVANCES ON SECURITY ARCHITECTURE FOR 5G TECHNOLOGY AND SERVICES

Shobowale, K. O., Mukhtar, Z., Yahaya, B., Ibrahim, Y., & Momoh, M. O. (2023) · International Journal of Computer Systems & Software Engineering · doi:10.15282/ijsecs.9.1.2023.3.0107 · Cited by 12

Energy-Efficient Wireless Federated Learning: A Secrecy Oriented Design via Sequential Artificial Jamming

Wang, T., Huang, N., Wu, Y., & Quek, T. Q. S. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3229277 · Cited by 12

Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

Fu, X., Xiong, L., Li, F., Yang, X., & Xiong, N. N. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3439437 · Cited by 11

Survey of Artificial Intelligence Model Marketplace

Qian, M., Musa, A. A., Biswas, M., Guo, Y., Liao, W., & Yu, W. (2025) · Future Internet · doi:10.3390/fi17010035 · Cited by 16

Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things

Wang, G., Yin, J.-L., Hossain, M. S., & Muhammad, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.015 · Cited by 13

Review of Recent Technologies for Tackling COVID-19

Alharbi, A. A., & Rahman, M. A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00841-z · Cited by 14

Acute Teaching Method of College Physical Skills Based on Mobile Intelligent Terminal

Wang, C. (2022) · Journal of Interconnection Networks · doi:10.1142/s0219265921470071 · Cited by 11

Blockchain Security Threats and Collaborative Defense: A Literature Review

Li, X., Cheng, J., Shi, Z., Liu, J., Zhang, B., Xu, X., Tang, X., & Sheng, V. S. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.040596 · Cited by 11

Rec-Def: A Recommendation-Based Defence Mechanism for Privacy Preservation in Federated Learning Systems

Sandeepa, C., Siniarski, B., Wang, S., & Liyanage, M. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3333560 · Cited by 11

Federated Learning Research: Trends and Bibliometric Analysis

Farooq, A., Feizollah, A., & Rehman, M. H. U. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_1 · Cited by 11

REPS-AKA3: A secure authentication and re- authentication protocol for LTE networks

Mobarhan, M. A., & Salamah, M. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103345 · Cited by 11

Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of VehiclesPreprint

Wang, R., Li, H., & Liu, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.01116 · Cited by 13

FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks

Castillo, J., Rieger, P., Fereidooni, H., Chen, Q., & Sadeghi, A. R. (2023) · Annual Computer Security Applications Conference · doi:10.1145/3627106.3627194 · Cited by 10

RRFL: A rational and reliable federated learning incentive framework for mobile crowdsensing

He, Q., Tian, Y., Wang, S., & Xiong, J. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.101977 · Cited by 10

Robust quantum federated learning with noise

Chen, L., Yan, L., & Zhang, S. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad4df2 · Cited by 11

Against network attacks in renewable power plants: Malicious behavior defense for federated learning

Wu, X., Jin, Z., Zhou, J., Liu, K., & Liu, Z. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110577 · Cited by 10

Healthcare Data Protection using Federated Learning Technology

Gobinath, B., & Shanmugam, T. K. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142903 · Cited by 10

Towards Trustworthy Collaborative Healthcare Data Sharing

Firdaus, M., & Rhee, K.-H. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385319 · Cited by 13

Analysis and evaluation of synchronous and asynchronous FLchain

Wilhelmi, F., Giupponi, L., & Dini, P. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109390 · Cited by 10

FedGroup-Prune: IoT Device Amicable and Training-Efficient Federated Learning via Combined Group Lasso Sparse Model Pruning

Chen, Z., Peng, J., Kang, J., & Niyato, D. T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3457871 · Cited by 10

Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization

Chaudhury, S. D., Morreddigari, L. R., Varun, M., Sengupta, T., Chakraborty, S., Sural, S., Vaidya, J. S., & Atluri, V. (2024) · IEEE Transactions on Privacy · doi:10.1109/tp.2024.3487819 · Cited by 11

Community-Oriented Duplex Privacy Amplification and Active Poisoning Resistance for Heterogeneous Federated Learning

Zhou, Z., Zhao, J., Yang, S., Li, H., Ma, T., & Xu, C. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3559108 · Cited by 10

A Joint Framework to Privacy-Preserving Edge Intelligence in Vehicular Networks

Firdaus, M., & Rhee, K.-H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25659-2_12 · Cited by 10

MEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs

Chen, G., Liu, X., Shorfuzzaman, M., Karime, A., Wang, Y., & Qi, Y. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3453186 · Cited by 9

Time-Efficient Blockchain-Based Federated Learning

Lin, R., Wang, F., Luo, S., Wang, X., & Moshe Zukerman, D. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3436862 · Cited by 8

Edge-Assisted Label-Flipping Attack Detection in Federated Learning

Alotaibi, N., Felemban, M., & Mahmood, S. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3496872 · Cited by 9

On Energy Optimization for Hierarchical Federated Learning With Delay Constraint Through Node Cooperation

Li, Z., Zou, S., & Guo, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3349393 · Cited by 9

Advances in authentication and security protocols for 5G networks: a comprehensive survey

Patel, S. K., Verma, S. B., Gupta, B. K., Singh, S. K., Naresh, E., & Pareek, P. K. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07317-2 · Cited by 7

DeepHealth: A Secure Framework to Manage Health Certificates Through Medical IoT, Blockchain and Deep Learning

Rakib, G. A., Islam, M. S., Rahman, M. A., Syed, A. M., Hossain, M. S., Alrajeh, N. A., & Saddik, A. E. (2021) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea52024.2021.9478691 · Cited by 8

Blockchain-based fairness-enhanced federated learning scheme against label flipping attack

Jin, S., Li, Y., Chen, X., Li, R., & Shen, Z. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103580 · Cited by 7

Machine Learning-Enabled 5G and 6G Networks: Methods, Challenges, and Opportunities

Owais, M., & Shongwe, T. C. (2026) · Applied Sciences · doi:10.3390/app16042071 · Cited by 6

Fast Personalized Federated Learning in Wireless Networks With Heterogeneous Data and Limited Communication Resources

Fan, S., Ni, J., & Tian, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3403256 · Cited by 6

Federated Learning and Explainable AI for Decentralized Fraud Detection in Financial Systems

Ande, B. R. (2025) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v10i35s.5921 · Cited by 6

Advancing 5G Security and Privacy with AI: A Survey

He, H., Fei, S., & Yan, Z. (2025) · ACM Computing Surveys · doi:10.1145/3744555 · Cited by 10

Fuzzy Semantic Classification of Multi-Domain E-Learning Concept

Ahmed, R., Ahmad, T., Almutairi, F. M., Qahtani, A. M., Alsufyani, A., & Almutiry, O. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-021-01776-8 · Cited by 6

Paving the Way for Distributed Artificial Intelligence Over the Air

Ma, G., Zhang, C., Dang, S., & Shihada, B. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3188051 · Cited by 6

Privacy Preserving Federated RSRP Estimation for Future Mobile Networks

Haliloğlu, Ö., Soykan, E. Ü., & Alabbasi, A. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682084 · Cited by 6

Improving 5G network performance for OFDM-IDMA system resource management optimization using bio-inspired algorithm with RSM

Jadhav, M. M., Deshpande, V., Midhunchakkaravarthy, D., & Waghole, D. S. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.06.031 · Cited by 6

Application of Mobile Learning in Higher English Education Systems Using Cognitive Web Services

Chen, J. (2023) · International Journal of e-Collaboration · doi:10.4018/ijec.316654 · Cited by 6

Multimedia Technology Based Interactive Translation Learning for Students

Wang, Z., & Na, H. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3588569 · Cited by 6

Privacy-preserving Decentralized Federated Deep Learning

Zhu, X., & Li, H. (2021) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3472634.3472642 · Cited by 8

Towards robust and stealthy communication for wireless intelligent terminals

Chen, L., Qiu, K., Zhang, Z., Yang, J., Li, Y., & Hu, J. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23063 · Cited by 5

Blockchain-Enabled Secure Federated Learning for Digital Twin Networks

Cai, L., Hu, Q., Jiang, T., & Niyato, D. T. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.007.2400141 · Cited by 5

RAIM: three-stage stackelberg game for hierarchical federated learning with reputation-aware incentive mechanism

Zuo, C., Xu, P., Song, Y., Lu, J., Yuan, C., & Li, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-16830-8 · Cited by 4

Optimising Decision Making on Communication Systems: The Federated Learning Approach

Stergiou, K. D., Psannis, K. E., Roumeliotis, Μ., Kokkonis, G., & Ishibashi, Y. (2021) · IEEE International Conference on Information, Communication and Networks (ICICN) · doi:10.1109/icicn52636.2021.9674002 · Cited by 4

Blockchain and Trustworthy Reputation for Federated Learning: Opportunities and Challenges

Javed, F., Mangues‐Bafalluy, J., Zeydan, E., & Blanco, L. (2024) · IEEE International Mediterranean Conference on Communications and Networking (MeditCom) · doi:10.1109/meditcom61057.2024.10621302 · Cited by 4

Unlocking potential of open source model training in decentralized federated learning environment

Pavlova, E., Melnikov, G., Yanovich, Y., & Frolov, A. (2025) · Blockchain Research and Applications · doi:10.1016/j.bcra.2024.100264 · Cited by 4

Ensuring Security Continuum from Edge to Cloud : Adaptive Security for IoT-based Critical Infrastructures using FL at the Edge

Arı, İ., Balkan, K., Pirbhulal, S., & Abie, H. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825993 · Cited by 4

A Novel Federated Learning Framework for Healthcare Applications Using Wearable Devices

Natarajan, R., Krishna, S., & Christodoss, P. R. (2025) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic63015.2025.10848974 · Cited by 4

Serverless federated learning: Decentralized spectrum sensing in heterogeneous networks

Çatak, F. Ö., Kuzlu, M., Dalveren, Y., & Özdemir, G. (2025) · Physical Communication · doi:10.1016/j.phycom.2025.102634 · Cited by 4

Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data

Tabatabai, S., Mohammed, I., Qolomany, B., Albaseer, A. M., Ahmad, K., Abdallah, M. M., & Al‐Fuqaha, A. (2022) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc55113.2022.9825004 · Cited by 5

A Blockchain-based Decentralized Federated Learning Framework with Dual-Committees Consensus

Lin, Z., Wu, Z., Zhu, J., & Huang, J. (2022) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3579654.3579700 · Cited by 4

Study and Analysis of Multiconnectivity for Ultrareliable and Low‐Latency Features in Networks and V2X Communications

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