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

From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis

Wang, F., Chen, J., Zheng, L., & Huang, X. (2026) · Journal of bone oncology · doi:10.1016/j.jbo.2026.100753 · Cited by 6

Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

Murugan, R., Paliwal, M., Patibandla, R. S. M. L., Shah, P., Balaga, T. R., Gurrammagari, D. R., Singaravelu, P., Yenduri, G., & Jhaveri, R. H. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558285074231120063921 · Cited by 5

FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing

Ma, J., Chen, L., Xu, J., & Yuan, Y. (2024) · Applied Sciences · doi:10.3390/app14114788 · Cited by 5

Optimizing Soil-Based Crop Recommendations with Federated Learning on Raspberry Pi Edge Computing Nodes

Mwawado, R. H., Zennaro, M., Nsenga, J., & Hanyurwimfura, D. (2024) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms62296.2024.10710260 · Cited by 5

Employing Federated Learning for the Implication of Digital Twin

Saeed, F., Shaheen, M., Umer, T., & Farooq, M. S. (2024) · Springer eBooks · doi:10.1007/978-3-031-73679-7_5 · Cited by 5

Bias in Federated Learning: Factors, Effects, Mitigations, and Open Issues

Benmalek, M., & Seddiki, A. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290605 · Cited by 6

Research on efficient data processing method for fog computing based on blockchain and federated learning

Kang, H., Wu, B., & Cao, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129529 · Cited by 5

Federated Learning Framework for Privacy-Preserving AI in Healthcare

Ammirata, G., Pezzullo, G., Contino, S., Martino, B. D., & Pirrone, R. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87778-0_31 · Cited by 5

A hierarchical blockchain architecture for federated learning in edge computing networks

Ren, S., & Lee, C. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07262-2 · Cited by 5

Linkage on security, privacy and fairness in federated learning: New balances and new perspectives

Wang, L., Zhu, T., Zhou, W., & Yu, P. S. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107874 · Cited by 11

MOHAWK: Mobility and Heterogeneity-Aware Dynamic Community Selection for Hierarchical Federated Learning

Farcas, A.-J., Lee, M., Kompella, R. R., Latapie, H. M., Veciana, G. A. D., & Mărculescu, R. (2023) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3576842.3582378 · Cited by 5

Multiple-compression-rate CSI feedback for mm-wave massive MIMO based on federated learning

Hu, B., Xu, H., Wang, Z., Li, Z., & Zhou, A. (2024) · Physical Communication · doi:10.1016/j.phycom.2024.102305 · Cited by 5

Overcoming Resource Bottlenecks in Vehicular Federated Learning: A Cluster-Based and QoS-Aware Approach

AbdulRahman, S., Bouachir, O., Otoum, S., & Mourad, A. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437842 · Cited by 5

Detecting Cyber Threats and Enhancing Security in IOT Devices: A Federated Learning Approach

Ogundipe, M. F., & Shi, X. (2024) · International Conference on Global Aeronautical Engineering and Satellite Technology (GAST) · doi:10.1109/gast60528.2024.10520759 · Cited by 5

LSH Models in Federated Recommendation

Dai, H., Zhu, M., & Gui, X. (2024) · Applied Sciences · doi:10.3390/app14114423 · Cited by 6

An IoT-Driven Reinforcement Learning Framework for Optimized Flow Management in Autonomous Systems

Balasubramanian, V., Aloqaily, M., Guizani, M., & Ouni, B. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3576585 · Cited by 5

Machine Learning Intrusion Detection as a Solution to Security and Privacy Issues in IoT: A Systematic Review

Darley, O. G., Adenowo, A. A., & Yussuff, A. I. O. (2022) · FUOYE Journal of Engineering and Technology · doi:10.46792/fuoyejet.v7i2.802 · Cited by 5

Collaborative Screening of COVID-19-like Disease from Multi-Institutional Radiographs: A Federated Learning Approach

Abdel‐Basset, M., Hawash, H., & Abouhawwash, M. (2022) · Mathematics · doi:10.3390/math10244766 · Cited by 5

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future DirectionsPreprint

Duan, Q., Huang, J. G., Hu, S., Deng, R., Lu, Z., & Yu, S. (2022) · doi:10.36227/techrxiv.21788450 · Cited by 5

Federated Learning for Intelligent IoT Systems: Background, Frameworks, and Optimization Techniques

Ray, P. P. (2024) · Wiley eBooks · doi:10.1002/9781394219230.ch13 · Cited by 5

ADVANCEMENTS IN FEDERATED LEARNING FOR SECURE DATA SHARING IN FINANCIAL SERVICES

Unuigbokhai, N. B., Oise, G. P., Akilo, B. E., Nwabuokei, O. C., Odimayomi, J. A., Bakare, S. K., & Atake, O. M. (2025) · FUDMA Journal of Sciences · doi:10.33003/fjs-2025-0905-3207 · Cited by 8

A Comprehensive Survey of Federated Open-World Learning

Cai, Z., Pang, J., Li, Y., Huang, Y., & Xie, Z. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3582580 · Cited by 4

Fed-SecTP: A Federated Learning-Based Framework for Secure Vehicle Trajectory Prediction Using Surrounding Vehicle Data

Li, B., Chen, X., Zhang, T., Sun, H., Dong, L., Chen, B., Zhuang, W., & Yin, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3599834 · Cited by 4

Secure and scalable smart grid IoT communication through quantum key distribution, homomorphic encryption, and federated learning

Shukla, P. K., Mishra, S., Tiwari, S., Pandey, A., & Almazmomi, N. K. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-08107-6 · Cited by 4

802.11bf Multiband Passive Sensing: Reusing Wi-Fi Signaling for Sensing

Picazo-Martínez, P., Barroso-Fernández, C., Calvillo, A., Groshev, M., Bernardos, C. J., de la Oliva, A., & Mourad, A. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3656659 · Cited by 4

Federated Learning for Pedestrian Detection in Vehicular Networks

Kumec, F. E., Reyhanoglu, A., Kar, E., Turan, B., Çöleri, S., Bennis, M., Elgabli, A., Gündüz, D., & Karaagac, S. (2023) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom58138.2023.10299783 · Cited by 5

Development of Pneumonia Patient Classification Model Using Fair Federated Learning

Kim, D., Oh, K., Kang, S., & Lee, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53827-8_15 · Cited by 4

RoboCon: A Modular Robotic Containerization, Orchestration, and Load Balancing Technique for Mixed Hierarchy Task Handling Across Computing Platforms

Roy, S., & Kalafatis, S. (2023) · International Conference on Control and Robotics (ICCR) · doi:10.1109/iccr60000.2023.10444794 · Cited by 4

Robust and Secure Aggregation Scheme for Federated Learning

Tang, W., Li, J., Dong, C., Miao, Y., Li, Q., Li, N., Deng, S., & Ji, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3509222 · Cited by 4

Federated-Learning-Enabled Cross-Modal Semantic Communication for 6G

Huang, R., Qiu, C., Chen, M., Zhang, C., & Zhu, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3590596 · Cited by 6

Gender Inequity in Engineering Higher Education: A Case Study of an American University in a Middle Eastern Country

Abbas, M. A., & Saab, S. S. (2022) · International Conference on Information and Education Technology (ICIET) · doi:10.1109/iciet55102.2022.9778952 · Cited by 4

Robust peer-to-peer learning via secure multi-party computation

Luo, Y., Luo, W., Zhang, R., Zhang, H., & Shi, Y. (2023) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2023.08.003 · Cited by 5

A survey on federated learning: evolution, applications and challenges

Shi, L. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/22/20231177 · Cited by 4

Remote Heart Rate Monitoring in Smart Environments From Videos With Self-Supervised Pretraining

Gupta, D., & Etemad, A. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3327623 · Cited by 4

Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical Vehicles

Xu, J., Kilfoyle, R., Xiong, Z., & Lu, L. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317551 · Cited by 4

Federated Learning for Resource Management in Edge Computing

Abdelgaber, Y. E., Ahmed, Y. A., Salem, M. A. ‐., & Salem, M. A.-G. (2023) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis58388.2023.10413933 · Cited by 4

An Improved NCF Model in Federated Recommendation Systems

Dai, H., Zhu, M., & Gui, X. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450566 · Cited by 5

Federated privacy-preserving collaborative filtering for on-device next app prediction

Saiapin, A., Balitskiy, G., Bershatsky, D., Katrutsa, A., Frolov, E., Frolov, A., Oseledets, I. V., & Kharin, V. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09395-0 · Cited by 4

Enabling Data-Driven Solubility Modeling at GSK: Enhancing Purge Predictions for Mutagenic Impurities

Vià, L. D., Depoortere, M., Willacy, R. D., Roberts, A. J., Sokkar, P., Fossepré, M., Ruba, A. A., & Zwierzyna, M. (2024) · Organic Process Research & Development · doi:10.1021/acs.oprd.4c00384 · Cited by 4

A Continuous Authentication Approach for Mobile Crowdsourcing Based on Federated Learning

Wazzeh, M., Ould‐Slimane, H., Talhi, C., Mourad, A., & Guizani, M. (2024) · IEEE Access · doi:10.1109/access.2024.3507695 · Cited by 4

CLDP=FATD: Secure Federated Averaging Threat Detection Framework for Intelligent Vehicle Sensor Networks Based on Client-Level Differential Privacy

Anyanwu, G. O., & Karimipour, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3521609 · 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

Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning

Jiang, R., Kumar, Y., & Kruger, D. (2025) · Applied Sciences · doi:10.3390/app15063004 · Cited by 4

Federated Learning Augmented Cybersecurity for SDN-Based Aeronautical Communication Network

Ali, M., Hu, Y. F., & Li, J.-P. (2025) · Electronics · doi:10.3390/electronics14081535 · Cited by 6

FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning

Cai, Y., Liu, H., Wang, X., Li, H., Wang, Z., Yang, C., & Cong, F. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3568583 · Cited by 4

An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning

Zhang, S. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575223 · Cited by 4

A Distributed Consensus Protocol for Sustainable Federated Learning

Alfauri, H., & Esposito, F. (2021) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft51509.2021.9492565 · Cited by 4

Multi-Agent Reinforcement Learning Based Fully Decentralized Dynamic Time Division Configuration for 5G and B5G Network

Chen, X., Chuai, G., & Gao, W. (2022) · Sensors · doi:10.3390/s22051746 · Cited by 8

A communication-efficient distributed machine learning scheme in vehicular network

Li, Y., Wu, C., Zhong, L., & Yoshinaga, T. (2022) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3538641.3561494 · Cited by 4

NIFL: A Statistical Measures-Based Method for Client Selection in Federated Learning

M’Haouach, M., Houdou, A., Alami, H., Fardousse, K., & Berrada, I. (2022) · IEEE Access · doi:10.1109/access.2022.3225407 · Cited by 4

Global Update Guided Federated Learning

Wu, Q., Liu, L., & Xue, S. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902315 · Cited by 4

RingFFL: A Ring-Architecture-Based Fair Federated Learning Framework

Han, L., Huang, X., Li, D., & Zhang, Y. (2023) · Future Internet · doi:10.3390/fi15020068 · Cited by 4

Federated learning for performance prediction in multi-operator environments

Lan, X., Taghia, J., Moradi, F., Khoshkholghi, M. A., Zec, E. L., Mogren, O., Mahmoodi, T., & Johnsson, A. (2023) · ITU Journal on Future and Evolving Technologies · doi:10.52953/pfyz9165 · Cited by 5

Towards Mutual Trust-Based Matching For Federated Learning Client Selection

Wehbi, O., Wahab, O. A., Mourad, A., Otrok, H., Alkhzaimi, H. A., & Guizani, M. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10182581 · Cited by 4

Compromise privacy in large-batch Federated Learning via model poisoning

Zhang, S., Huang, J., Zhang, Z., Li, P., & Qi, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119421 · Cited by 6

Comparation of Federated and Centralized Learning for Image Classification

Mar’i, F., Supianto, A. A., & Bachtiar, F. A. (2023) · PIKSEL Penelitian Ilmu Komputer Sistem Embedded and Logic · doi:10.33558/piksel.v11i2.7367 · Cited by 4

Collaborative Learning in Kernel-Based Bandits for Distributed Users

Salgia, S., Vakili, S., & Zhao, Q. Q. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3325925 · Cited by 4

Privacy Preserving Federated Learning Solution for Security of Industrial Cyber Physical Systems

Majidi, S. H., & Asharioun, H. (2021) · Springer eBooks · doi:10.1007/978-3-030-76613-9_11 · Cited by 4

On Attacks To Federated Learning and a Blockchain-empowered Protection

Esposito, C., Sperlí, G., Moscato, V., & Zhao, Z. (2022) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc49033.2022.9700723 · Cited by 4

Network Intrusion Detection Empowered with Federated Machine LearningPreprint

Nasir, M. U., Mehmood, S., Khan, M. A., Zubair, M., Khan, F. N., & Lee, Y. (2023) · Research Square · doi:10.21203/rs.3.rs-3350992/v1 · Cited by 4

The Synergy of Artificial Intelligence and Blockchain in 6G Spectrum Management

Murugan, R., Yenduri, G., Maran, P., & Gadekallu, T. R. (2024) · doi:10.1002/9781394201235.ch10 · Cited by 4

FedTWA: A Federated Learning Model for Supply Chain Demand Forecasting

Wei, J., Chen, X., Shen, H., Cao, Y., & Zhang, S. (2024) · International Conference on Big Data Economy and Information Management (BDEIM) · doi:10.1145/3724154.3724275 · Cited by 3

Differential Privacy Decentralized Federated Learning for Internet of Vehicles Over Time-Varying Unbalanced Networks

Zhao, Z., Liu, Z., Zhang, C., & Wei, M. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3585176 · Cited by 4

Enhancing Online Transaction Fraud Detection via Heterogeneous Source Models

Tang, H., Wang, C., & Zhu, H. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3599992 · Cited by 3

Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare

Nawaz, A., Irfan, M., Yu, X., Aldawsari, H., Alsisi, R. H., Zou, Z., & Westerlund, T. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3620115 · Cited by 3

Adaptive Federated Edge Intelligence for Real-Time Cyberthreat Detection in Resource-Constrained IoT Environments: A Lightweight Deep Learning Approach

Rahmati, M., & Rahmati, N. (2025) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-025-00582-0 · Cited by 3

Efficient Mobile-Cloud Collaborative Aggregation for Federated Learning With Latency Resilience

Tang, W., Li, J., Zhang, X., Miao, Y., Su, Z., & Deng, R. H. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3642433 · Cited by 3

AI-Driven energy forecasting in smart buildings: A federated deep learning framework for Edge-Fog systems

Achy, J. A., Harb, H., & Makhoul, A. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108490 · Cited by 3

Trust and Transparency in IoT: The Role of Federated Learning and Blockchain

Kumar, R., Padmavathi, U., & Agrawal, N. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111173 · Cited by 3

Learning with Euler Collaborative Representation for Robust Pattern Analysis

Zhou, J., Wang, G., Zeng, S., & Zhang, B. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3625235 · Cited by 3

Federated Learning For Smoke and Fire Detection Models Optimization

Ahmed, Y. A., Salama, M. A., Salem, M. A. ‐., & Afifi, S. M. (2024) · National Radio Science Conference · doi:10.1109/nrsc61581.2024.10510473 · Cited by 3

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models

Coutinho‐Almeida, J., Cruz-Correia, R. J., & Rodrigues, P. P. (2024) · Scientific Reports · doi:10.1038/s41598-024-61371-1 · Cited by 3

Personalized Comfort Features in Software-defined Vehicles Using Federated Learning

Gül, B. C., Devarakonda, N., Jazdi, N., & Weyrich, M. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10710704 · Cited by 3

Federated Learning Frameworks: A survey

Hkima, M., Boudali, I., & Abdellatif, T. (2024) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa63423.2024.10912632 · Cited by 3

FedTLU: Federated Learning with Targeted Layer Updates

Park, J.-I., & Joe-Wong, C. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888369 · Cited by 3

A trust-based global expert system for disease diagnosis using hierarchical federated learning

Al-Mulla, F. M., & Almulla, M. A. (2025) · Journal of Engineering Research · doi:10.1016/j.jer.2025.03.001 · Cited by 3

FedBS: Solving data heterogeneity issue in federated learning using balanced subtasks

Su, C., Wu, J., Zhang, R., Kang, Z., Xia, H., & Zhang, C. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100322 · Cited by 3

Joint Optimization for Volatile Federated Learning in Vehicular Edge Computing: A Deep Reinforcement Learning Approach

Li, Y., Feng, L., Mei, M., Ali, A., & Shah, Z. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3564013 · Cited by 3

Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization

Sun, Y., Hu, G. X., Teng, Y., & Cai, D. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978241 · Cited by 7

FedSensor: Federated Learning Framework for Secure Sensor-Based IoT at the Extreme Edge

Ferraz, N., Silva, A., Guelfi, A. E., Ueda, E. T., & Kofuji, S. T. (2025) · IEEE Access · doi:10.1109/access.2025.3595490 · Cited by 5

A Framework for Verifiable and Auditable Collaborative Anomaly Detection

Santin, G., Skarbovsky, I., Fournier, F., & Lepri, B. (2022) · IEEE Access · doi:10.1109/access.2022.3196391 · Cited by 3

Application of Federated Learning in Health Care Sector for Malware Detection and Mitigation Using Software Defined Networking Approach

Panagoda, D., Malinda, C., Wijetunga, C., Rupasinghe, L., Bandara, B., & Liyanapathirana, C. (2022) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon55314.2022.9909488 · Cited by 3

A Self-Organizing IoT Service Perception Algorithm Based on Human Visual Direction-Sensitive System

Yang, Z., Zhang, J., Jiang, Y., & Jin, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3223039 · Cited by 3

Machine Learning Data Markets: Trading Data using a Multi-Agent System

Baghcheband, H., Soares, C., & Reis, L. P. (2022) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat55865.2022.00073 · Cited by 3

An Innovative Approach to PMI Analysis and Enhancing Information Flow Efficiency

Qiao, H., Hu, S., Zhang, L., Li, J. P., & You, L. (2023) · IEEE Access · doi:10.1109/access.2023.3291461 · Cited by 3

Incentive Mechanism Design for Vertical Federated Learning

Yang, N., & Cheung, M. H. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279735 · Cited by 3

Remaining Time Prediction for Collaborative Business Processes with Privacy Preservation

Cao, J., Wang, C., Guan, W., Qian, S., & Zhao, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48424-7_4 · Cited by 3

PSO-Enabled Federated Learning for Detecting Ships in Supply Chain Management

Supriya, Y., Srivastava, G., Kandati, D. R., Yenduri, G., Victor, N., Anusha, S., & Gadekallu, T. R. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8132-8_31 · Cited by 3

FL-PERF: Predicting TCP Throughput with Federated Learning

Aung, H. N., & Ohsaki, H. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437294 · Cited by 3

A Game-Theoretic Federated Learning Approach for Ship Detection from Aerial Images

Mary, D. R. K., Supriya, Y., Victor, N., Gadekallu, T. R., & Paek, J. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437089 · Cited by 3

Enhancing Email Security in Consumer Electronics with a Hybrid Deep Learning Approach

Gaurav, A., Gupta, B. B., Chui, K. T., & Arya, V. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444468 · Cited by 3

Preserving differential privacy in neural networks for foreign object detection with heterogeneity-based noising among distributed devices

Wang, M., Wang, Q.-Y., Zhang, Y., Zhang, Z., Feng, Y., & Cao, Y. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06243-1 · Cited by 3

Making Federated Learning Accessible to Scientists: The AI4EOSC Approach

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