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

Gradient-based learning applied to document recognition

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

Learning Multiple Layers of Features from Tiny Images

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

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215

MIMIC-III, a freely accessible critical care database

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

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772

The multimodal brain tumor image segmentation benchmark (BRATS)

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322

Federated learning: Challenges, methods, and future directions

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210

The MovieLens Datasets: History and Context

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

Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

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

A Docker-based federated learning framework design and deployment for multi-modal data stream classification

Nandi, A., Xhafa, F., & Kumar, R. (2023) · Computing · doi:10.1007/s00607-023-01179-5 · Cited by 21

SLMFed: A Stage-Based and Layerwise Mechanism for Incremental Federated Learning to Assist Dynamic and Ubiquitous IoT

You, L., Guo, Z., Zuo, B.-R., Chang, Y., & Yuen, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3353793 · Cited by 21

DeFTA: A plug-and-play peer-to-peer decentralized federated learning framework

Zhou, Y., Shi, M., Tian, Y., Ye, Q., & Lv, J. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120582 · Cited by 26

M2FD: Mobile malware federated detection under concept drift

Augello, A., Paola, A. D., & Re, G. L. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104361 · Cited by 20

Machine Learning-Based Distributed Denial of Services (DDoS) Attack Detection in Intelligent Information Systems

Alhalabi, W., Gaurav, A., Arya, V., Zamzami, I. F., & Aboalela, R. (2023) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.327280 · Cited by 20

Dynamic gradient filtering in federated learning with Byzantine failure robustness

Colosimo, F., & Rango, F. D. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.044 · Cited by 21

Privacy-Preserving Realization of Fuzzy Clustering and Fuzzy Modeling Through Vertical Federated Learning

Zhu, X., Wang, D., Pedrycz, W., & Li, Z. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3320680 · Cited by 19

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi, B. K., Khan, F. A., & Mahmood, S. (2024) · Applied Sciences · doi:10.3390/app14072720 · Cited by 23

Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks

Asheralieva, A., Niyato, D. T., & Wei, X. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3543295 · Cited by 22

ROFED-LLM: Robust Federated Learning for Large Language Models in Adversarial Wireless Environments

Wang, H., Yin, Z., Chen, B., Zeng, Y., Yan, X., Zhou, C., & Li, A. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3590975 · Cited by 20

PureChain-enhanced federated learning for dynamic fault tolerance and attack detection in distributed systems

Ahakonye, L. A. C., Nwakanma, C. I., Lee, J. M., & Kim, D. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100354 · Cited by 19

Federated learning with stochastic quantization

Li, Y., Li, W., & Xue, Z. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23056 · Cited by 20

Digital twin-enabled domain adaptation for zero-touch UAV networks: Survey and challenges

McManus, M., Cui, Y., Zhang, J. Z., Hu, J., Moorthy, S. K., Mastronarde, N., Bentley, E. S., Medley, M. J., & Guan, Z. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110000 · Cited by 24

FedChain-Hunter: A reliable and privacy-preserving aggregation for federated threat hunting framework in SDN-based IIoT

Duy, P. T., Quyen, N. H., Khoa, N. H., Tran, T.-D., & Pham, V.-H. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100966 · Cited by 19

Smart Intrusion Detection in IoT Edge Computing Using Federated Learning

Fenanir, S., & Semchedine, F. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370505 · Cited by 19

Federated Deep Learning-based Intrusion Detection Approach for Enhancing Privacy in Fog-IoT Networks

Bensaid, R., Labraoui, N., & Salameh, H. B. (2023) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms59855.2023.10325826 · Cited by 19

An Incentive Mechanism for Long-Term Federated Learning in Autonomous Driving

Fu, Y., Li, Z., Liu, S., Li, C., Yu, F. R., & Cheng, N. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3348498 · Cited by 19

A comprehensive analysis of model poisoning attacks in federated learning for autonomous vehicles: A benchmark study

Almutairi, S., & Barnawi, A. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103295 · Cited by 21

The Internet of Things under Federated Learning: A Review of the Latest Advances and Applications

Liu, Z., Yang, X., Li, M., Wang, J., & Lv, Z. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.058926 · Cited by 23

Towards green machine learning: challenges, opportunities, and developments

Pedrycz, W. (2022) · Journal of Smart Environments and Green Computing · doi:10.20517/jsegc.2022.16 · Cited by 18

Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection

Cai, C., Guo, J., Song, X., Zhang, Y., Wu, J., Tang, S., Jia, Y. M., Xing, Z., & Li, Q. (2023) · Sustainability · doi:10.3390/su15021617 · Cited by 18

A Hybrid Aggregation Approach for Federated Learning to Improve Energy Consumption in Smart Buildings

Abboud, A., Brahmia, M., Abouaïssa, A., Shahin, A. M., & Mazraani, R. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10183138 · Cited by 18

HDHRFL: A hierarchical robust federated learning framework for dual-heterogeneous and noisy clients

Jiang, Y., Wang, D., Song, B., & Luo, S. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.049 · Cited by 18

FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer

Gao, X., Yang, X., Yu, H., Yan, K., & Li, T. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00424 · Cited by 20

Wireless for Machine Learning: A Survey

Hellström, H., da Silva, J. M. B., Amiri, M. M., Chen, M., Fodor, V., Poor, H. V., & Fischione, C. (2022) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000114 · Cited by 23

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

de Souza, R. M. M., Holm, A., Biczyk, M., & de Castro, L. N. (2024) · Electronics · doi:10.3390/electronics13163157 · Cited by 19

Communication-Efficient Online Federated Learning Strategies for Kernel Regression

Gogineni, V. C., Werner, S., Huang, Y.-F., & Kuh, A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3218484 · Cited by 17

FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment

Wong, Y., Tham, M.-L., Kwan, B.-H., & Owada, Y. (2023) · Italian National Conference on Sensors · doi:10.3390/s23052494 · Cited by 18

Advancing Federated Learning with Granular Computing

Pedrycz, W. (2023) · Fuzzy Information and Engineering · doi:10.26599/fie.2023.9270001 · Cited by 17

FedCLS: Class-Aware Federated Learning in a Heterogeneous Environment

Bhatti, D. M. S., & Nam, H. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3278023 · Cited by 17

Efficient Information Dissemination in Blockchain-Enabled Federated Learning for IoV

Ghimire, B., Rawat, D. B., & Rahman, A. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3346296 · Cited by 17

Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT

Fan, B., Su, X. J., Tarkoma, S., & Hui, P. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3639825 · Cited by 17

Communication-Efficient Hybrid Federated Learning for E-Health With Horizontal and Vertical Data Partitioning

Yu, C., Shen, S., Wang, S., Zhang, K., & Hai, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3383748 · Cited by 18

Game-Theoretic Design of Quality-Aware Incentive Mechanisms for Hierarchical Federated Learning

Hu, G., Han, J., Lu, J., Yu, J., Qiu, S., Peng, H., Zhu, D., & Li, T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3394170 · Cited by 18

Breast Cancer Prediction Using Shapely and Game Theory in Federated Learning Environment

Supriya, Y., & Chengoden, R. (2024) · IEEE Access · doi:10.1109/access.2024.3424934 · Cited by 20

Distributed Learning in Intelligent Transportation Systems: A Survey

Li, Q., Zhou, W., & Zheng, X. (2024) · Information · doi:10.3390/info15090550 · Cited by 20

A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases

Gulati, S., Guleria, K., Goyal, N., AlZubi, A. A., & Castilla, Á. K. (2024) · IEEE Access · doi:10.1109/access.2024.3493946 · Cited by 21

Machine learning-based inertia estimation in power systems: a review of methods and challenges

Diggikar, S., Patil, A., Satyapal, K. S., & Samad, K. (2025) · Energy Informatics · doi:10.1186/s42162-025-00496-7 · Cited by 21

Local Differential Privacy-Based Federated Learning under Personalized Settings

Wu, X., Xu, L., & Zhu, L. (2023) · Applied Sciences · doi:10.3390/app13074168 · Cited by 16

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang, G. F., Gu, C., Li, J., Wang, J., Chen, X., & Zhang, H. (2023) · Drones · doi:10.3390/drones7060380 · Cited by 17

FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework

Younis, R., Ahmadi, Z., Hakmeh, A., & Fisichella, M. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599354 · Cited by 18

CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication

Wazzeh, M., Arafeh, M., Sami, H., Ould‐Slimane, H., Talhi, C., Mourad, A., & Otrok, H. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103987 · Cited by 16

Evaluation of Applying Federated Learning to Distributed Intrusion Detection Systems Through Explainable AI

Oki, A., Ogawa, Y., Ota, K., & Dong, M. (2024) · IEEE Networking Letters · doi:10.1109/lnet.2024.3465516 · Cited by 17

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities

Wehbi, O., Arisdakessian, S., Guizani, M., Wahab, O. A., Mourad, A., Otrok, H., khzaimi, H. A., & Ouni, B. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3476950 · Cited by 18

Clustered Hierarchical Distributed Federated Learning

Gou, Y., Wang, R., Li, Z., Imran, M. A., & Zhang, L. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838880 · Cited by 17

Ensuring Privacy and Communication Efficiency Through Partial Parameter Exchange in Collaborative Learning for Vehicular Applications

Saravanarajan, V. B., Sridhar, S., Balasubramanian, V. R., Prabagarane, N., Mertens, J. S., Cafiso, S. D., Galluccio, L., Morabito, G., & Pappalardo, G. (2024) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom61746.2024.10646275 · Cited by 15

On the Feasibility of Federated Learning for Neurodevelopmental Disorders: ASD Detection Use-Case

Shamseddine, H., Otoum, S., & Mourad, A. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001248 · Cited by 15

DPP-Based Client Selection for Federated Learning with NON-IID DATA

Zhang, Y., Xu, C., Yang, H. H., Wang, X., & Quek, T. Q. S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095604 · Cited by 17

Secure short-term load forecasting for smart grids with transformer-based federated learning

Sievers, J., & Blank, T. (2023) · International Conference on Clean Electrical Power · doi:10.1109/iccep57914.2023.10247363 · Cited by 15

Vulnerability detection based on federated learning

Zhang, C., Yu, T., Liu, B., & Xin, Y. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107371 · Cited by 15

The effectiveness of using interactive visual multimedia technology intervention in improving the literacy skills of children in rural China

Dong, H., Han, Q., Liu, P., & Apuke, O. D. (2024) · Learning and Motivation · doi:10.1016/j.lmot.2024.101964 · Cited by 15

Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset

Shiri, I., Salimi, Y., Sirjani, N., Razeghi, B., Bagherieh, S., Pakbin, M., Mansouri, Z., Hajianfar, G., Avval, A. H., Askari, D., et al. (2024) · Medical Physics · doi:10.1002/mp.16964 · Cited by 17

Robust Federated Unlearning

Sheng, X., Bao, W., & Ge, L. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679817 · Cited by 17

Dew Intelligence: Federated learning perspective

Guberović, E., Lipić, T., & Čavrak, I. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00274 · Cited by 15

Federated Learning for Anomaly Detection: A Case of Real-World Energy Storage Deployment

Wang, X., Chen, Y., & Dobre, O. A. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838875 · Cited by 15

Resilient Distributed Classification Learning Against Label Flipping Attack: An ADMM-Based Approach

Wang, X., Fang, C., Yang, M., Wu, X., Zhang, H., & Cheng, P. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3264918 · Cited by 15

FLEX-IDS: A secure and explainable federated intrusion detection framework for Edge-of-Things environments under adversarial conditions

Chowdhury, A. P., Nur, F. N., Islam, A. H. M. S., Alam, K., Karim, A., & Shah, M. A. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110827 · Cited by 17

Two-Phase Deep Reinforcement Learning of Dynamic Resource Allocation and Client Selection for Hierarchical Federated Learning

Chen, X., Li, Z., Ni, W., Wang, X., Zhang, S., Xu, S., & Pei, Q. (2022) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc55456.2022.9880724 · Cited by 14

Cyber Security and 5G-assisted Industrial Internet of Things using Novel Artificial Adaption based Evolutionary Algorithm

Singh, S. P., Piras, G., Viriyasitavat, W., Kariri, E., Yadav, K., Dhiman, G., Vimal, S., & Khan, S. B. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02230-7 · Cited by 14

Dynamic, Context-Aware Cross-Layer Orchestration of Containerized Applications

Sofia, R. C., Dykeman, D., Urbanetz, P., Galal, A., & Dave, D. A. (2023) · IEEE Access · doi:10.1109/access.2023.3307026 · Cited by 16

A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

Bibi, A., Sampedro, G. A., Almadhor, A. S., Javed, A. R., & Kim, T.-H. (2023) · Technologies · doi:10.3390/technologies11050121 · Cited by 14

FlwrBC: Incentive Mechanism Design for Federated Learning by Using Blockchain

Cam, N. T., & Kiet, V. T. (2023) · IEEE Access · doi:10.1109/access.2023.3320045 · Cited by 14

Anonymous Federated Learning via Named-Data Networking

Agiollo, A., Bardhi, E., Conti, M., Fabbro, N. D., & Lazzeretti, R. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.11.009 · Cited by 18

Federated learning for resource allocation in vehicular edge computing-enabled moving small cell networks

Zafar, S., Jangsher, S., & Zafar, A. (2023) · Vehicular Communications · doi:10.1016/j.vehcom.2023.100695 · Cited by 14

Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning

Atitallah, S. B., Driss, M., & Ghézala, H. B. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.326 · Cited by 14

A prototype-assisted clustered federated learning for big data security and privacy preservation

Jiang, Y., Wang, D., Song, B., & Du, X. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.032 · Cited by 16

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

Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset

Peng, S., Yang, Y., Mao, M., & Park, D.-S. (2022) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2022.02.020 · Cited by 14

Peer-to-Peer Personalized Federated Transfer Learning for Battery State of Health Estimation of Vehicles

Wang, T., & Dong, Z. Y. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3449334 · Cited by 13

Multimodal large language models for medical image diagnosis: Challenges and opportunities

Zhang, A., Zhao, E., Wang, R., Zhang, X., Wang, J., & Chen, E. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104895 · Cited by 16

Federated learning and differential privacy in clinical health: Extensive survey

Odera, D. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0113 · Cited by 14

Exploring Machine Learning Intrusion Detection: Addressing Security and Privacy Challenges in IoT - A Comprehensive Review

Krishnamoorthy, G., & Sistla, S. M. K. (2023) · Journal of Knowledge Learning and Science Technology · doi:10.60087/jklst.vol2.n2.p125 · Cited by 13

Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case

Hwang, S. O., & Majeed, A. (2024) · Applied Sciences · doi:10.3390/app14104100 · Cited by 15

Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework

Zhang, H., Bosch, J., & Olsson, H. H. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107600 · Cited by 19

Federated Learning via Attentive Margin of Semantic Feature Representations

Michieli, U., Toldo, M., & Özay, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3209865 · Cited by 13

Privacy Preservation Using Federated Learning for Credit Card Transactions

Hema Priya, N., Rathika, P. D., & Pushparaj, A. (2023) · International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) · doi:10.1109/iciscois56541.2023.10100577 · Cited by 13

Rebirth of Distributed AI—A Review of eHealth Research

Khan, M. A., & Alkaabi, N. (2021) · Sensors · doi:10.3390/s21154999 · Cited by 13

HELCFL: High-Efficiency and Low-Cost Federated Learning in Heterogeneous Mobile-Edge Computing

Cui, Y., Cao, K., Zhou, J., & Wei, T. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774662 · Cited by 13

Navigating the fusion of federated learning and big data: a systematic review for the AI landscape

Haripriya, R., Khare, N., Pandey, M., & Biswas, S. (2025) · Cluster Computing · doi:10.1007/s10586-024-05070-6 · Cited by 15

Towards Bilateral Client Selection in Federated Learning Using Matching Game Theory

Wehbi, O., Arisdakessian, S., Wahab, O. A., Otrok, H., Otoum, S., & Mourad, A. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001251 · Cited by 12

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

IDS-MA: Intrusion Detection System for IoT MQTT Attacks Using Centralized and Federated Learning

Omotosho, A., Qendah, Y., & Hammer, C. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00093 · Cited by 13

Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks

Fan, L., Zhang, X., Zhao, Y., Sood, K., & Yu, S. (2023) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2023.3326331 · Cited by 12

Decentralized federated domain generalization with cluster alignment for fault diagnosis

Xu, D., Jia, M., Chen, T., Liu, Y., Chai, T., & Yang, T. (2024) · Control Engineering Practice · doi:10.1016/j.conengprac.2024.105951 · Cited by 13

Network Traffic Anomaly Detection Based on Federated Learning

Luan, Y. (2024) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice61279.2024.10498908 · Cited by 13

FedSCCS: Hierarchical Clustering with Multiple Models for Federated Learning

Talasso, G. U., Souza, A. M. D., Bittencourt, L. F., Cerqueira, E., Loureiro, A. A. F., & Villas, L. A. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622346 · Cited by 12

Towards web 4.0: frameworks for autonomous AI agents and decentralized enterprise coordination

Gürpinar, T. (2025) · Frontiers in Blockchain · doi:10.3389/fbloc.2025.1591907 · Cited by 12

Privacy-preserving federated learning in asynchronous environment using homomorphic encryption

Gupta, M., Kumar, M., & Dhir, R. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104116 · Cited by 12

Chronos: Accelerating Federated Learning With Resource Aware Training Volume Tuning at Network Edges

Liu, Y., Zhang, X., Zhao, Y., He, Y., Yu, S., & Zhu, K. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3218155 · Cited by 12

Data-aware Hierarchical Federated Learning via Task Offloading

Ma, M., Wu, L., Liu, W., Chen, N., Shao, Z., & Yang, Y. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000924 · Cited by 12

Lightweight federated learning for STIs/HIV prediction

Nguyen, T. P. V., Yang, W., Tang, Z., Xia, X., Mullens, A. B., Dean, J. A., & Li, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-56115-0 · Cited by 12

Italian, European, and international neuroinformatics efforts: An overview

Redolfi, A., Archetti, D., Francesco, S. D., Crema, C., Tagliavini, F., Lodi, R., Ghidoni, R., Wheeler‐Kingshott, C. A. M. G., Alexander, D. C., & D'Angelo, E. U. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15854 · Cited by 12

SVFLC: Secure and Verifiable Federated Learning With Chain Aggregation

Li, N., Zhou, M. L., Yu, H., Chen, Y., & Yang, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3330813 · Cited by 12

TrustDFL: A Blockchain-Based Verifiable and Trusty Decentralized Federated Learning Framework

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