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

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Communication-Efficient Learning of Deep Networks from Decentralized Data

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MIMIC-III, a freely accessible critical care database

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Advances and Open Problems in Federated Learning

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The multimodal brain tumor image segmentation benchmark (BRATS)

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Federated Machine Learning: Concept and Applications

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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

The MovieLens Datasets: History and Context

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Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318

DCFL: Dynamic Clustered Federated Learning under Differential Privacy Settings

Augello, A., Falzone, G., & Re, G. L. (2023) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops56833.2023.10150285 · Cited by 14

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence.Preprint

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2021) · arXiv (Cornell University) · Cited by 11

Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic ReviewPreprint

Ali, A., Ilahi, I., Qayyum, A., Mohammed, I., Al‐Fuqaha, A., & Qadir, J. (2021) · doi:10.36227/techrxiv.14945433 · Cited by 13

FeLebrities: A User-Centric Assessment of Federated Learning Frameworks

Riviera, W., Galazzo, I. B., & Menegaz, G. (2023) · IEEE Access · doi:10.1109/access.2023.3312579 · Cited by 10

Federated Learning in Smart Agriculture: An Overview

Le, D.-D., Dao, M.-S., Tran, A.-K., Nguyen, T.-B., & Le-Thi, H.-G. (2023) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse59128.2023.10299497 · Cited by 10

Assessing Research Productivity and Quality Across Disciplines in the School of Arts and Sciences at the Lebanese American University

Sayyed, K. (2024) · SAGE Open · doi:10.1177/21582440241237050 · Cited by 14

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

Zheng, J., Liu, X., Ling, Z., & Hu, F. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3399404 · Cited by 10

Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains

Schoinas, Ι., Triantafyllou, A., Ioannidis, D., Tzovaras, D. K., Drosou, A., Votis, K., Λάγκας, Θ., Argyriou, V., & Sarigiannidis, P. G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3458088 · Cited by 10

Cyber Attacks Prevention Toward Prosumer-Based EV Charging Stations: An Edge-Assisted Federated Prototype Knowledge Distillation Approach

Zou, L., Vo, Q. H., Kim, K. T., Le, H. Q., Thwal, C. M., Zhang, C., & Hong, C. S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3517621 · Cited by 10

Federated Learning: An Approach for Managing Data Privacy and Security in Collaborative Learning

Jaswal, R., Panda, S. N., & Khullar, V. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965328724241218110637 · Cited by 13

DAG-Based Swarm Learning Approach in Healthcare: A Survey

Gana, D., & Jamil, F. (2025) · IEEE Access · doi:10.1109/access.2025.3531216 · Cited by 12

Federated learning frameworks in smart e-healthcare: A systematic literature review with bias evaluation

Panda, S., Dubey, R., Jena, B., Pareek, V., Tsai, L.-W., & Saxena, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112747 · Cited by 11

A Comprehensive Survey on Split-Fed Learning: Methods, Innovations, and Future Directions

Hukkeri, G. S., Goudar, R. H., Dhananjaya, G. M., Rathod, V. N., & Ankalaki, S. (2025) · IEEE Access · doi:10.1109/access.2025.3547641 · Cited by 14

Enhanced Traffic Carbon Emissions Data Sharing and Modeling via Blockchain and Personalized Federated Learning in IIoT Ecosystem

Zhang, X., Liu, H., & Yang, F. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3571952 · Cited by 10

GRAF-IDS: graph-based clustering as aggregation for federated intrusion detection system in IoT network

Rezaie, H., Taheri, R., Shojafar, M., & Foh, C. H. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11385-1 · Cited by 10

Efficient Multi-Layer Stochastic Gradient Descent Algorithm for Federated Learning in E-health

Yu, C., Shen, S., Wang, S., Zhang, K., & Zhao, H. Y. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839100 · Cited by 10

Compression scenarios for Federated Learning in Smart Manufacturing

Nasri, S. A. E. M., Ullah, I., & Madden, M. G. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.239 · Cited by 10

ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection

Guo, Z., You, L., Liu, S., He, J., & Zuo, B.-R. (2023) · Mathematics · doi:10.3390/math11081867 · Cited by 10

Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence

Bar, S., Prasad, P. W. C., & Sayeed, M. S. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.053861 · Cited by 10

Towards secure and reliable aggregation for Federated Learning protocols in healthcare applications

Arbaoui, M., Brahmia, M., & Rahmoun, A. (2022) · International Conference on Software Defined Systems (SDS) · doi:10.1109/sds57574.2022.10062923 · Cited by 10

Decentralized Federated Learning Methods for Reducing Communication Cost and Energy Consumption in UAV Networks

Pan, D., Khoshkholghi, M. A., & Mahmoodi, T. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-31891-7_2 · Cited by 9

Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning

Li, J., Sun, Q., & Sun, F. (2023) · Data Science Journal · doi:10.5334/dsj-2023-031 · Cited by 11

Resource Management and Optimization in Internet of Vehicles for Hierarchical Federated Learning

Yuan, T., Chen, L., Jiang, Y., Chen, H., Gong, W., & Gu, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3486775 · Cited by 11

Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning

Zhong, Y. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00096-7 · Cited by 9

Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study

Xie, Y., Wang, X., Yang, H., Zhang, J., Wang, H., Yan, Z., Yang, J., Yan, Z., Hao, Z., Liu, P., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1534117 · Cited by 9

Medical & healthcare robotics: a roadmap for enhanced precision, safety, and efficacy

Iakovidis, D. K., Vartholomeos, P., Gall, A. L., Cianchetti, M., Ozioko, O., Dahiya, R., Mazomenos, E. B., Vasconcelos, F., Stoyanov, D. V., Philpott, J., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae09bf · Cited by 10

Task Offloading for CAVs Edge Computing Environment: Taxonomy, Critical Review, and Future Road Map

Kumar, B., Bhardwaj, A., & Sahu, D. P. (2025) · ACM Computing Surveys · doi:10.1145/3783984 · Cited by 9

AI in Education: Improving Quality for Both Centralized and Decentralized Frameworks

Madathil, N. T., Alrabaee, S., Al-kfairy, M., Damseh, R., & Belkacem, A. N. (2023) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon54358.2023.10125139 · Cited by 10

Federated Transfer Learning for Energy Efficiency in Smart Buildings

Campos, E. M., González-Vidal, A., Hernández-Ramos, J. L., & Skármeta, A. (2023) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps57453.2023.10225844 · Cited by 9

QoS-aware edge server placement for collaborative predictive maintenance in industrial internet of things

Mehta, A., & Verma, R. K. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06210-w · Cited by 9

FL-MAC-RDP: Federated Learning over Multiple Access Channels with Rényi Differential Privacy

Wu, S., Yu, M., Ahmed, M. A. M., Qian, Y., & Tao, Y.-H. (2021) · International Journal of Theoretical Physics · doi:10.1007/s10773-021-04867-0 · Cited by 9

Federated Learning for Privacy-Preserving Internet of Things

Abdel‐Basset, M., Moustafa, N., Hawash, H., & Ding, W. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-89025-4_9 · Cited by 9

Progressive Adoption of RINA in IoT Networks: Enhancing Scalability and Network Management via SDN Integration

Sarabia-Jácome, D., Giménez-Antón, S., Liatifis, A., Grasa, E., Catalán, M., & Pliatsios, D. (2024) · Applied Sciences · doi:10.3390/app14062300 · Cited by 9

Massive MIMO for Serving Federated Learning and Non-Federated Learning Users

Farooq, M., Vu, T. T., Ngo, H. Q., & Tran, L. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3277037 · Cited by 8

Dynamic Federated Learning Aggregation for Enhanced Intrusion Detection in IoT Attacks

Umair, M., Tan, W.-H., & Foo, Y. (2024) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic60209.2024.10463247 · Cited by 8

Federated Learning for Internet of Medical Healthcare: Issues and Challenges

Chelani, N. R., Tripathy, S., Kumhar, M., Bhatia, J., Saxena, V. P., Tanwar, S., & Nayyar, A. (2024) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v25i5.2905 · Cited by 8

Revolutionizing Internet of Underwater Things with Federated Learning

Shaheen, M., Farooq, M. S., Umer, T., & Tran, T. A. (2024) · The Springer series in applied machine learning · doi:10.1007/978-3-031-64642-3_12 · Cited by 8

FedDC: Label Noise Correction With Dynamic Clients for Federated Learning

Giap, T.-T., Kieu, T.-D., Le, T., & Tran, T.-H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3509962 · Cited by 8

Optimizing Federated Learning: Addressing Key Challenges in Real-World Applications

Kang, N., Lim, Y., & Im, J. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3558910 · Cited by 8

Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

Chen, J., Zhuo, S., He, J., Qiu, W., Zhang, Q., Xiong, Z., Zheng, Z., Tang, Y., Chen, M., Wang, C., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3560635 · Cited by 8

Federated Learning Augmented with Convolutional Neural Networks for Brain Cancer Classification

Awotunde, J. B., Abikoye, C. O., Brahma, B., Oladipupo, E. T., & Bandyopadhyay, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.523 · Cited by 10

Energy Management Systems for Maritime Microgrids: A Comprehensive Review of Intelligent Optimization Strategies

Khan, A. A., Timilsina, L., Muriithi, G., Arsalan, A., Moghassemi, A., Papari, B., Özkan, G., Edrington, C. S., Boghrabadi, N. S., & Wang, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3613231 · Cited by 9

Federated Learning and 5G/6G‐Based Internet of Medical Things (IoMT): Applications, Key Enabling Technologies, Open Issues and Future Research Directions

Ahad, A., Ahmed, K. I., Ullah, F., Sheikh, M. A., Tahir, M., Hayajneh, M., & Pires, I. M. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70065 · Cited by 9

Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning

Seo, S., Lee, J., Ko, H., & Pack, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3262582 · Cited by 8

NIDS-FLGDP: Network Intrusion Detection Algorithm Based on Gaussian Differential Privacy Federated Learning

Du, J., & Yang, K. (2023) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624500488 · Cited by 8

Federated Learning for the Metaverse: A Short Survey

Yenduri, G., Kandati, D. R., Srivastava, G., Supriya, Y., Ramalingam, M., Gadekallu, T. R., & Awaysheh, F. M. (2023) · International Conference on Intelligent Metaverse Technologies & Applications (iMETA) · doi:10.1109/imeta59369.2023.10294796 · Cited by 8

An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning

Mbonu, W. E., Maple, C. R., & Epiphaniou, G. (2023) · Electronics · doi:10.3390/electronics12214543 · Cited by 8

Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

Wazzeh, M., Arafeh, M., Ould‐Slimane, H., Talhi, C., Mourad, A., & Otrok, H. (2023) · Cyber Security in Networking Conference (CSNet) · doi:10.1109/csnet59123.2023.10339744 · Cited by 8

On-Device Continual Learning With STT-Assisted-SOT MRAM-Based In-Memory Computing

Zhang, F., Sridharan, A., Hwang, W., Xue, F., Tsai, W., Wang, S. X., & Fan, D. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3371268 · Cited by 8

Multi-Objective Distributed Client Selection in Federated Learning-Assisted Internet of Vehicles

Cha, N., & Chang, L. (2024) · Sensors · doi:10.3390/s24134180 · Cited by 8

EuProGigant: A decentralized Federated Learning Approach based on Compute-to-Data and Gaia-X

Gehrer, R., Dumss, S., Gast, F., Wünschel, W., Schwill, F., Šoša, M., Zhou, S., Ristow, G. H., Gharagyozyan, T., Heistracher, C., et al. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.07.060 · Cited by 8

Context-Aware Support for Cardiac Health Monitoring Using Federated Machine Learning

Ogbuabor, G. O., Augusto, J. C., Moseley, R., & van Wyk, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91100-3_22 · Cited by 8

Performance-Aware Client and Quantization Level Selection Algorithm for Fast Federated Learning

Seo, S., Lee, J., Ko, H., & Pack, S. (2022) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc51071.2022.9771600 · Cited by 8

Federated Learning: Data Privacy and Cyber Security in Edge-Based Machine Learning

White, J., & Legg, P. (2023) · Springer eBooks · doi:10.1007/978-3-031-34006-2_6 · Cited by 8

Federated Learning for Beginners: Types, Simulation Environments, and Open Challenges

Panigrahi, M., Bharti, S., & Sharma, A. (2023) · International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) · doi:10.1109/ic2e357697.2023.10262769 · Cited by 8

CRAS-FL: Clustered resource-aware scheme for federated learning in vehicular networks

AbdulRahman, S., Bouachir, O., Otoum, S., & Mourad, A. (2024) · Vehicular Communications · doi:10.1016/j.vehcom.2024.100769 · Cited by 8

Late to the party? On-demand unlabeled personalized federated learning

Amosy, O., Eyal, G., & Chechik, G. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00218 · Cited by 7

A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer

Al‐Betar, M. A., Abasi, A. K., Alyasseri, Z. A. A., Fraihat, S., & Mohammed, R. F. (2024) · Algorithms · doi:10.3390/a17040160 · Cited by 7

An IoMT Enabled Iterative Artificial Bee Colony Approach Using Federated Learning for Detection of Heart Disease

Khan, N. A., Akhtar, M. M., Siddiqi, A. M. U., Rashid, K. A., Rajeyyagari, S., Khalid, M. N., & Ahmad, M. K. (2024) · Springer tracts in nature-inspired computing · doi:10.1007/978-981-97-7344-2_6 · Cited by 7

Hierarchical federated transfer learning in digital twin-based vehicular networks

Zia, Q., Zhu, S., Wang, H., Iqbal, Z., & Li, Y. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100303 · Cited by 7

Online Federated Deep Probabilistic Learning-Based Smart Healthcare on Multi-Cloud Systems

Kotb, Y., Oueida, S., Mostafa, N., & Ali, N. (2025) · IEEE Access · doi:10.1109/access.2025.3557877 · Cited by 9

A comprehensive review of ground support equipment scheduling for aircraft ground handling services

Zhou, P., Shen, Y., Zheng, Y., Zheng, Y., Guo, B., & Du, Y. (2025) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2025.104341 · Cited by 8

Federated learning for 5G-enabled infrastructure inspection with UAVs

Lekidis, A. (2022) · Energy Informatics · doi:10.1186/s42162-022-00254-z · Cited by 7

Multi-player evolutionary game of federated learning incentive mechanism based on system dynamics

Yang, P., Zhang, H., Gao, F., Xu, Y., & Jin, Z. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126739 · Cited by 7

A Dropout-Tolerated Privacy-Preserving Method for Decentralized Crowdsourced Federated Learning

Chen, T., Wang, X., Dai, H., & Yang, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3315730 · Cited by 8

Wind power prediction method based on cloud computing and data privacy protection

Zhang, L., Zhu, S., Su, S., Chen, X., Yang, Y. T., & Zhou, B. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00679-9 · Cited by 7

Federated deep learning models for detecting RPL attacks on large-scale hybrid IoT networks

Albishari, M., Li, M., Ayoubi, M., Alsanabani, A. A., & Tian, J. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110837 · Cited by 8

Federated Episodic Learning to Extrapolate Unseen From Seen Conditions for Industrial IoT Monitoring

Li, B., & Zhao, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3496927 · Cited by 7

A 3D Model-Based Framework for Real-Time Emergency Evacuation Using GIS and IoT Devices

Tyagi, N., Singh, J., Singh, S., & Sehra, S. S. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13120445 · Cited by 9

A strategy to the reduction of communication overhead and overfitting in Federated Learning

Barros, A., Rosário, D., Cerqueira, E., & da Fonseca, N. L. S. (2021) · doi:10.5753/wgrs.2021.17181 · Cited by 7

Data-Driven Quickest Change Detection for Securing Federated Learning for Internet-of-Vehicles

Ghimire, B., Rawat, D. B., & Rahman, A. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685333 · Cited by 7

Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case

Shaer, I., & Shami, A. A. (2022) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc55113.2022.9824540 · Cited by 7

Cyclic Federated Learning Method Based on Distribution Information Sharing and Knowledge Distillation for Medical Data

Yu, L., & Huang, J. (2022) · Electronics · doi:10.3390/electronics11234039 · Cited by 7

On Leveraging FemtoClouds for Federated Learning

Gedawy, H. K., Harras, K. A., & Erbad, A. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2100136 · Cited by 8

Attribute recognition for person re-identification using federated learning at all-in-edge

Girija, S., Baker, T., Ahmed, N., Khedr, A. M., Aghbari, Z. A., Jha, A., Соболев, К., Ahmadi‐Asl, S., & Phan, A. H. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100793 · Cited by 9

Towards dependable 6G networksPreprint

Ahmad, I., Rodriguez, F., Huusko, J., & Seppänen, K. (2022) · doi:10.36227/techrxiv.20237358 · Cited by 7

A Survey of AI-Based Attack Detection Models on the Edge-IIoTset Dataset

Kulrujiphat, S., & Kulrujiphat, P. (2024) · doi:10.1109/icbim63313.2024.10823532 · Cited by 6

Self-adaptive Federated Learning in Internet of Things Systems: A Review

Aljohani, A., RANA, O. F., & Perera, C. (2025) · ACM Computing Surveys · doi:10.1145/3725527 · Cited by 7

A Privacy-Enhanced Method for Privacy-Preserving and Verifiable Federated Learning

Wang, X., Chen, T., Dai, H., Long, P., Yang, H., Xiong, Z., & Susilo, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3561262 · Cited by 7

Decentralized federated learning meets Physics-Informed Neural Networks

Alfano, G., Greco, S., Mandaglio, D., Parisi, F., Shahbazian, R., & Trubitsyna, I. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113717 · Cited by 6

DECENTRALIZED DEEP LEARNING IN HEALTHCARE: ADDRESSING DATA PRIVACY WITH FEDERATED LEARNING

Oise, G. P., Onwuzo, C. J., Fole, M., Oyedotun, S. A., Odimayomi, J. A., Unuigbokhai, N. B., Ejenarhome, P. O., & Akilo, B. E. (2025) · FUDMA Journal of Sciences · doi:10.33003/fjs-2025-0906-3714 · Cited by 6

Quantum Federated Learning: A Comprehensive Survey

Uddin, M. R., Shaon, S., Rahman, R., Nguyen, D. C., Dobre, O. A., & Niyato, D. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3644750 · Cited by 30

CHPFL: Clustered adaptive hierarchical federated learning for edge-level personalization

Song, L., Li, J., Jiang, H., Wei, S., & Guo, Y. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100343 · Cited by 7

Federated Transfer Learning for Energy Efficient Privacy-preserving Medical Image Classification

Ahmed, M. S., & Giordano, S. (2022) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom54947.2022.9982789 · Cited by 9

Federated Learning for 6G HetNets' Physical Layer Optimization

Bartsiokas, I. A., Gkonis, P. K., Papazafeiropoulos, A., Kaklamani, D. I., & Venieris, I. S. (2023) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch070 · Cited by 6

Adaptive Edge-Level Personalization on Hierarchical Federated Learning

Song, L., Li, J., Jiang, H., Wei, S., & Guo, Y. (2023) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc59175.2023.10253826 · Cited by 6

Facilitating AI-Based CSI Feedback Deployment in Massive MIMO Systems With Learngene

Li, X., Guo, J., Wen, C.-K., Geng, X., & Jin, S. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3381211 · Cited by 6

PureFed: An Efficient Collaborative and Trustworthy Federated Learning Framework Based on Blockchain Network

Putra, M. A. P., Karna, N. B. A., Alief, R. N., Zainudin, A., Kim, D., Lee, J., & Sampedro, G. A. (2024) · IEEE Access · doi:10.1109/access.2024.3411091 · Cited by 8

Anomaly-Based Network Intrusion Detection Using Hybrid CNN, Bi-LSTM Deep Learning Techniques

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