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

Gradient-based learning applied to document recognition

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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

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Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated IntelligencePreprint

Qiao, Y., Le, H. Q., Raha, A. D., Tran, P.-N., Adhikary, A., Zhang, M., Nguyen, L. X., Huh, E., Niyato, D. T., & Hong, C. S. (2025) · doi:10.36227/techrxiv.174762883.35612385/v1 · Cited by 3

Federated Learning: From Origins to Modern Applications and Challenges

Bharathi, M., Srinivas, T. A. S., & Bhuvaneswari, M. (2024) · Journal of Information Technology and Cryptography. · doi:10.48001/joitc.2024.1229-38 · Cited by 3

Two step Federated Learning approach in Brain Stroke identification and classification via fine-tuning EfficientNetB0 network at Edge nodes

Turjya, S. M., Singh, R., Sarkar, P., Rout, S., Swain, S., & Bandyopadhyay, A. (2024) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon62179.2024.10744248 · Cited by 2

Federated and Centralized Machine Learning for Cell Segmentation: A Comparative Analysis

Bruschi, S., Esposito, M., Raggiunto, S., Belli, A., & Pierleoni, P. (2025) · Electronics · doi:10.3390/electronics14071254 · Cited by 2

MAB-Based Online Client Scheduling for Decentralized Federated Learning in the IoT

Chen, Z., Zhang, X., Wang, S., & Wang, Y. (2025) · Entropy · doi:10.3390/e27040439 · Cited by 2

Secflh- Defending Federated Learning-Based IoT Health Prediction Systems Against Poisoning Attacks

Liyanage, S., Weerawardhane, V., Kheminda, J., Siriwardhana, Y., Weerasinghe, T. N., & Liyanage, M. (2025) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit63408.2025.11037014 · Cited by 2

A Byzantine-robust federated learning against adversarial–majority attacks

Shi, Y., Hu, X., Bai, S., Liu, Y., & Lin, H. W. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07619-7 · Cited by 2

Privacy Preserving Distributed Image Processing Using Federated Learning and CNNs

Ranasinghe, N. K., Liyanage, P., & Kruglova, L. V. (2025) · IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) · doi:10.1109/iscaie64985.2025.11080797 · Cited by 2

Multiple Reconfigurable Intelligent Surfaces Aided V2X Offloading Networks: A Federated Reinforcement Learning-Based Approach

Zeng, M., Zhao, Y., Wang, J., & Fei, Z. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3592718 · Cited by 2

Federated Learning for Internet of Underwater Things Based on Lightweight Distillation and Data Refinement

Jiang, B., Fei, J., Luo, F., Liu, Y., & Song, H. H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3605230 · Cited by 2

Def-Ag: An energy-efficient decentralized federated learning framework via aggregator clients

Park, J., Woo, S., & Lee, J. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108114 · Cited by 2

Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review

Gulati, S., Guleria, K., Goyal, N., & Dogra, A. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056400866250923175325 · Cited by 3

Scheduling and Securing Asynchronous Federated Learning Through Cooperative Jamming

Ni, F., Zhou, Z., Ni, W., Chen, X., Pan, G., Sun, Y., Zhang, S., & Jamalipour, A. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3623377 · Cited by 2

Federated Learning for Secure and Privacy‐Aware Internet of Medical Things: Taxonomy, Emerging Applications, Open Challenges, and Future Directions

Wani, R. U. Z., & Can, Ö. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70432 · Cited by 3

From privacy to trust in the agentic era: a taxonomy of challenges in trustworthy federated learning through the lens of trust report 2.0

Rodríguez-Barroso, N., García-Márquez, M., Luzón, M. V., & Herrera, F. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104236 · Cited by 5

From Cryptocurrencies to Collaborative Risk Management: A Review of Decentralized AI Approaches

Gürpinar, T., Gulum, M. A., & Martinelli, M. (2025) · FinTech · doi:10.3390/fintech4040074 · Cited by 2

Literary Review for a Enhanced Personalized Recommendations with Federated Learning and Multimodel Emotion Recognition

Rodrigues, C., Cruz, J., Gaspar, J., Rodrigues, A., Ribeiro, I., Martinho, D., Santos, J., & Marreiros, G. (2025) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta66210.2025.11202847 · Cited by 2

Efficient Asynchronous Federated Edge Learning Oriented Tasks Scheduling and Resources Allocation in Dynamic Multitasks MEC Networks

Zhao, Z., Zhang, H., Ding, H., Liu, W., & Yuan, D. (2026) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3648735 · Cited by 2

Federated Open-Set Fault Diagnosis for Unknown Bearing Fault Detection

Xu, D., Jia, M., Chen, T., Liu, Y., Chen, D., Chai, T., & Yang, T. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.113917 · Cited by 2

Edge AI and Federated Learning: A Post-Cloud Paradigm for The Next Decade of Intelligent Computing

William, P., Ayasrah, F. T., Singh, T. P., & Pardeshi, D. B. (2025) · International Conference on Software Defined Systems (SDS) · doi:10.1109/sds68531.2025.11409871 · Cited by 2

A Federated FHIR-Based Interoperability Framework for Multi-Site Heart Failure Monitoring: The RETENTION Project

Vasileiou, N., Giannakopoulou, O., Manta, O., Bromis, K., Vagenas, T. P., Kouris, I., Roumpi, M., Koumakis, L., Goletsis, Y., Haritou, M., et al. (2026) · Computers · doi:10.3390/computers15040212 · Cited by 2

Hierarchical Federated Learning Approach for IoT Attacks Classification

Umair, M., Tan, W.-H., & Foo, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3686024 · Cited by 2

Model optimization in federated intrusion detection systems: a survey of aggregation, communication, and personalization

Ma, X., Yang, L., Wang, H., & Chen, L. (2026) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-026-00901-w · Cited by 2

Lean Six Sigma and Design of Experiments: An Empirical Case Study From the Dairy Industry

Nader, J. (2022) · Interdisciplinary Research in Technology and Management (IRTM) · doi:10.1109/irtm54583.2022.9791828 · Cited by 2

Performance vs. Privacy: Evaluating the Performance of Predicting Second Primary Cancer in Lung Cancer Survivors with Privacy-preserving Approaches

Hong, J.-F., & Tseng, Y. (2022) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi56158.2022.9926935 · Cited by 2

Low Resource Vs High Resource solutions for Federated learning sentiment analysis

Sethi, A., Chui, K. T., Gupta, B. B., Arya, V., Castiglione, A., & Zhang, Z. (2023) · International Conference on Consumer Electronics · doi:10.1109/icce56470.2023.10043544 · Cited by 2

PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

Zhu, Y., Sun, S., Liu, C., Tian, X., He, J., & Xiao, S. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.026691 · Cited by 2

MTFL: Multi-task Federated Learning for Classification of Healthcare X-Ray Images

Kumar, P., Sinha, I. K., & Singh, K. P. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31417-9_43 · Cited by 2

Efficient secure aggregation for privacy-preserving federated learning based on secret sharing

Jin, X., Yao, Y., & Yu, N. (2023) · JUSTC · doi:10.52396/justc-2022-0116 · Cited by 5

A Privacy‐Preserving and Edge‐Collaborating Architecture for Personalized Mobility

Jian, W., He, J., Chen, J., & Cai, M. (2023) · Journal of Advanced Transportation · doi:10.1155/2023/8333560 · Cited by 2

Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge Computing

Xu, Y., Li, L., Cong, P., & Zhou, J. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00129 · Cited by 2

FedCSR: A new cluster sampling based on rotation mechanism in horizontal federated learning

Dai, Q., Yan, T., & Ren, P. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.08.016 · Cited by 3

Anonymous federated learning framework in the internet of things

Du, R., Chuan, L., & Gao, Y. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7901 · Cited by 2

Double Momentum Backdoor Attack in Federated Learning

Panigrahi, S., Bouacida, N., & Mohapatra, P. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i1.3644 · Cited by 2

AdaptPSOFL: Adaptive Particle Swarm Optimization-Based Layer Offloading Framework for Federated Learning

Verma, R., & Benedict, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7093-3_40 · Cited by 2

Privacy-preserving continuous learning for MobileSAM via Federated Learning

Yoon, J. S., Park, Y. M., Zhang, C., & Hong, C. S. (2023) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc58710.2023.10318929 · Cited by 2

Enhancing Real-Time Training of Heterogeneous UAVs Using a Federated Teacher-Student Self-Training Framework

Nikam, P., Shah, D., Sahu, A., Goveas, N., & Vidhyadharan, S. (2023) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom58377.2023.10356303 · Cited by 2

Enhancing Logistics Optimization

Wang, L., Liu, G., & Hamam, H. (2024) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.344039 · Cited by 2

A Review on Suitability of Vertical Federated Learning in Smart City Platforms

Soares, K., & Shinde, A. A. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-61475-0_10 · Cited by 2

DPLE: A Privacy-Enhanced and Straggler-Resilient Distributed Learning Framework for Smart Cloud

Xue, Y., Li, J., & Wu, J. (2024) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud62736.2024.00009 · Cited by 2

Federated Learning-Based Resource Allocation in RSU Assisted Moving Network

Zafar, S., Jangsher, S., & Zafar, A. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570762 · Cited by 2

Mecanismo para Mitigar Ataques de Envenenamento de Modelo no Aprendizado Federado

Morais, M. G. O., da Costa, J. B. D., Gonzalez, L. F. G., Souza, A. M. D., & Villas, L. A. (2024) · doi:10.5753/courb.2024.3398 · Cited by 2

A Communication-Efficient Machine Learning Framework for the Internet of Vehicles

Ghimire, B., & Rawat, D. B. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3508776 · Cited by 3

Application of Federated Learning in Smart Grid Fault Diagnosis: Privacy Protection and Efficiency Improvement

Mei, H., Deng, C., Liu, H., Zeng, Y., & Zeng, Y. (2024) · International Conference on Energy, Power and Grid (ICEPG) · doi:10.1109/icepg63230.2024.10775979 · Cited by 2

FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning

Mueller, B., Street, W. N., Baek, S., Lin, Q., Yang, H. J., & Huang, Y. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825304 · Cited by 2

Societal impacts and opportunities of federated learning

Curl, J., & Xie, X. (2025) · Chinese Journal of Sociology · doi:10.1177/2057150x251314299 · Cited by 4

Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Berkani, M. R. A., Chouchane, A., Himeur, Y., Abdennebi, A., Sağıroğlu, Ş., & Amira, A. (2024) · International Conference on Telecommunications and Intelligent Systems (ICTIS) · doi:10.1109/ictis62692.2024.10893909 · Cited by 2

Lightweight verifiable federated learning based on matrix theory

Niu, S., Zhang, Y., Wang, W., Hu, L., & Nan, X. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130579 · Cited by 2

Federated Learning Optimization: Model Update Difference Minimization and Functional Based Adaptive Learning Rate Adjustment

Rong, Z., Liu, Y., Dai, M., Savazzi, S., & Kianoush, S. (2025) · World Conference on Computing and Communication Technologies (WCCCT) · doi:10.1109/wccct65447.2025.11027927 · Cited by 2

DFedMQ: Decentralized Federated Learning Based on Dynamic Selection Collaboration and Topology Optimization

Jiang, B., Wu, J., Yue, G., Cui, X., Wang, J., & Song, H. H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3584095 · Cited by 2

Fed-CNN-IDS: A Privacy-Preserving Federated Learning-Based CNN Intrusion Detection System for IoMT

Kharoubi, K., Cherbal, S., Akkal, M., & Gawanmeh, A. (2025) · International Conference on Communication, Computing, Networking, and Control in Cyber-Physical Systems (CCNCPS) · doi:10.1109/ccncps66785.2025.11135629 · Cited by 2

Efficient and Privacy-preserving Distributed Learning in Cloud-Edge Computing Systems

Jiang, Y., Zhang, K., Qian, Y., & Hu, R. Q. (2021) · ACM Workshop on Wireless Security and Machine Learning (WiseML) · doi:10.1145/3468218.3469044 · Cited by 2

Breaking Hardware Boundaries of IoT Devices via Inverse Feature Completion

Gao, Y., Ong, Y., Gong, M.-G., Jiang, F., Zhang, Y.-Q., & Wang, S.-F. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3222191 · Cited by 2

Evaluation of Fusion Techniques for Multi-modal Sentiment Analysis

Shinde, R., Udatewar, P., Nandargi, A., Mohan, S., Agrawal, R., & Nirale, P. (2022) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic55218.2022.10088291 · Cited by 2

Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

Li, Z., & Wang, W. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.028670 · Cited by 2

Evaluating the Impact of Local Data Imbalance on Federated Learning Performance for IoT Anomaly Detection

Singh, J., & Gupta, S. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307898 · Cited by 2

Personalized Federated Learning With Multiview Geometry Structure

Yan, Y., Wang, S., Sun, F., & Tong, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3425435 · Cited by 2

Robust Federated Opportunistic Learning in the Presence of Label Quality Disparity

Li, C., Li, G., & Varshney, P. K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3125341 · Cited by 2

Analysis of an Online English Teaching Model Application Based on Improved Multiorganizational Particle Population Optimization Algorithm

Wang, F. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6232987 · Cited by 2

Towards dependable 6G networksPreprint

Rodriguez, F., Ahmad, I., Huusko, J., & Seppänen, K. (2022) · doi:10.36227/techrxiv.20237358.v1 · Cited by 2

Adaptive Client Selection with Personalization for Communication Efficient Federated LearningPreprint

de Souza, A. T., Maciel, F., da Costa, J. B. D., Bittencourt, L. F., Cerqueira, E., Loureiro, A. A. F., & Villas, L. A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4654118 · Cited by 2

An Integrated Federated Learning and Meta-Learning Approach for Mining Operations

Munagala, V. S. K. R., Singh, S., Thudumu, S., Logothetis, I., Bhandari, S., Bhandari, A., Mouzakis, K., & Vasa, R. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8388-9_31 · Cited by 1

A federated learning-based approach for land surface temperature prediction considering cost-efficiency in seasonally frozen ground region

Lu, Y., Mei, G., Yang, Y., & Ding, H. (2024) · Georisk Assessment and Management of Risk for Engineered Systems and Geohazards · doi:10.1080/17499518.2024.2337384 · Cited by 1

Smart radio access selection and slice allocation for differentiated traffic management over 6G heterogeneous networks

González, C. C., & Murroni, M. (2024) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3625468.3652915 · Cited by 1

Federated Deep Learning for Solving an Image Classification Problem on a Desktop Grid System

Kurochkin, I. I., Prun, A. I., & Balaev, A. A. (2024) · Physics of Particles and Nuclei · doi:10.1134/s1063779624030560 · Cited by 1

Non-static TinyML for ad hoc networked devices

Fragkou, E., & Katsaros, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00015-4 · Cited by 1

Government and economic regulations on federated learning in emerging digital healthcare systems

Musa, A., Abdulfatai, A. O., Jacob, S. E., & Oluyemi, D. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00012-6 · Cited by 1

Networked Federated Meta-Learning Over Extending Graphs

Cheema, M. A., Gogineni, V. C., Rossi, P. S., & Werner, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3443467 · Cited by 1

A Novel Blockchain-Based Privacy-Preserved Data Sharing Using Deep Maxout Network in Federated Learning

Suganthi, S., & Kala, T. S. (2024) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026824500202 · Cited by 1

Social Welfare Maximization for Federated Learning with Network Effects

Li, X., Luo, Y., Luo, B., & Huang, J. (2024) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3641512.3686394 · Cited by 1

Self-enhanced multi-task and split federated learning framework for RIS-aided cell-free systems

Urakami, T., Jia, H., Chen, N., & Okada, M. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101406 · Cited by 1

Enhancing Fraud Detection in Enterprise Resource Planning Systems through Federated Learning: A Comparative Study

Schnepf, J., Dieterich, C., Öztürk, O., Hirt, R., & Scheuermann, B. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825489 · Cited by 1

B2RAM: Design and practical implementation of a secured information management framework for dynamic resource allocation using a novel 2-Tier blockchain model

Chakraborty, T., Ashish, A., Das, P., Jha, S., Saha, D., & Shahi, S. K. (2025) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2025.103096 · Cited by 1

Contract Design for Adaptive Federated Learning

Yang, N., Cui, Y., & Cheung, M. H. (2024) · Globecom · doi:10.1109/globecom52923.2024.10901817 · Cited by 1

Cyber Security Concerns and Mitigation Strategies in Federated Learning: A Comprehensive Review

Ahir, P., & Parikh, M. C. (2025) · NFSU Journal of Cyber Security Digital Forensic · doi:10.69490/jcsdf920250526 · Cited by 1

Learn More by Using Less: Distributed Learning with Energy-Constrained Devices

Pereira, R., Vaca-Rubio, C. J., & Blanco, L. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00076 · Cited by 1

Distributed Cybersecurity Preemptiveness on Industrial IoT Network Infrastructures

Akpan, E. E., Matthew, U. O., Oladele, H. A., Okochi, P. I., Falebita, O. S., Adebola, N. T., Ebong, G. N., & Andrew-Vitalis, N. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1717-5.ch004 · Cited by 1

Conv-ScaleNet: A Multiscale Convolutional Model for Federated Human Activity Recognition

Ting, X. W., Pang, Y. H., Lim, Z. Y., Yin, O. S., & Hiew, F. S. (2025) · AI · doi:10.3390/ai6090218 · Cited by 1

SignDefence: Byzantine-Robust Federated Learning with Sign Direction and Leaky ReLU

Moyeen, M. A., Kaur, K., Agarwal, A., Ricardo Manzano, S., Zaman, M., & Goel, N. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161809 · Cited by 1

Coded Robust Aggregation for Distributed Learning Under Byzantine Attacks

Li, C., Xiao, M., & Skoglund, M. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3624620 · Cited by 1

DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones

Singampalli, A., Gufran, D., & Pasricha, S. (2025) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin66788.2025.11212926 · Cited by 1

The Role of Federated Learning in AI-Powered Integrated Healthcare Solutions

Ahmed, K., Baranwal, A., Sharma, N., Garg, P., & Singh, N. S. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3306-9.ch015 · Cited by 1

Software Engineering Aspects of Federated Learning Libraries: A Comparative Survey

Alsghaier, H., & Zhao, T. (2025) · Software · doi:10.3390/software4040028 · Cited by 1

FUBA: Backdoor Federated Learning via Federated Unlearning

Sheng, X., Bao, W., Guo, Y., & Fu, S. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3630110 · Cited by 2

Safeguarding federated learning models in intelligent internet of medical things against data poisoning attacks

Mondal, S., Ghosh, T., & Patra, S. S. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00244-z · Cited by 4

Decentralized cybersecurity in smart grids: Leveraging location-fedavg for rapid threat detection and adaptive resilience

Nakkeeran, M., Narayanan, V. A., Sivakumar, P. B., & Balamurugan, S. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.108518 · Cited by 2

Application of Machine learning in predicting cancer complications using longitudinal Data: A systematic review and Meta-Analysis

Zamani, A. S., Motwakel, A., Alluhayb, A., Akhtar, M. M., Ayub, R., Abdelrahim, M. A. A., Mohamed, S. S. I., & Ahmad, N. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106217 · Cited by 1

VEHFSL: Hybrid Federated Split Learning for Resource-Constrained Vehicular Networks

Li, C., Zhao, Z., Zhang, H., & Yuan, D. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3641157 · Cited by 1

Leveraging cutting-edge technologies into energy management smart buildings: An era of revolution

Achy, J. A., Harb, H., & Makhoul, A. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112938 · Cited by 2

Vertical federated learning of multi-layer parallel pooling-based vision transformer for assembly quality prediction

Leng, J., Li, R., Chen, Z., Zhang, Q., Zhou, X. R., Qi, Q., Liu, Q., & Chen, X. (2025) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/09544054251405700 · Cited by 1

Mobility-Aware Cross-Region Federated Mutual Knowledge Distillation in Internet of Vehicles

Huang, X., Hu, W., Xiao, S., Xiao, Y., Tang, Y., Zhou, M., & Chen, Q. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3652689 · Cited by 1

Bi-directional distillation enabling progressive lightweight model training in edge computing-based federated learning

Chen, Y., Dai, W., Qin, Z., & Han, J. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114712 · Cited by 1

Combating Phishing with AI: Advanced Strategies for Modern Cyber Defense

Purkait, A., Shikhare, R., Pillai, S., Pathak, P., & Yadav, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7526-5_44 · Cited by 1

Quantum-Enhanced Federated Learning with Explainable Multimodal Intelligence for Heart Disease Risk Stratification, Progression Analysis, and Personalized Care

sali, R., & Harikrishna, T. (2025) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca66444.2025.11383196 · Cited by 1

Adaptive Federated Learning with Dynamic Client Selection and Heterogeneous Data Distribution for Large-Scale IoT Applications

Danda, J. M. R., Kesavan, M. K., Ivsl, H., Tammineedi, P., & Kolli, A. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402570 · Cited by 1

A Systematic Mapping of federated learning operations and features: Architecture, communication and aggregation models

Kukkaro, A., Moreschini, S., Taibi, D., & Hästbacka, D. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112863 · Cited by 1

Learning with Euler non-negative representation for robust pattern analysis

Zhou, J., Lin, Z., Zhang, Q., & Gu, J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132229 · Cited by 1

Distributed crop disease detection with deep Q-network agent decision-making in a compressed state space

Yue, R., He, L., Ye, R., Wang, J., Li, Y., Bai, T., Gu, S., & Wang, J. (2026) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2026.02.006 · Cited by 1

Advanced Battery State Monitoring in Electric Vehicles: A Comprehensive Review of Spatiotemporal Dynamics and Federated Learning Approaches

Ren, J., Li, M., Wang, Y., Xin, Y., Yuan, Y., Ye, G., Xu, G., Liu, Y., & Li, Y. Q. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3686511 · Cited by 1

A quantum-assisted federated learning framework for physics-regulated wind speed forecasting under extreme weather conditions

Hong, Y.-Y., & Medina, D. J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132719 · Cited by 1

Federated learning: Challenges and future work

Hossain, M. B., & Rahman, R. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.15.2.0711 · Cited by 5

Towards_Scalable_and_Secure_Federated_Learning__Key_Issues_and_Future_Research.pdf

Daniels, M., Gallus, S., & Wood, R. (2025) · Figshare · doi:10.6084/m9.figshare.28238567

Federated Learning: A Survey on Privacy-Preserving Collaborative IntelligencePreprint

Rahman, R. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2504.17703 · Cited by 3

Federated Learning With Deep Learning: A Comprehensive Survey

Mnkash, S. H., Alawy, F. A. A., & Ali, I. T. (2026) · Iraqi Journal of Computer Communication Control and System Engineering · doi:10.33103/2617-3352.1524

Federated Learning: Healthcare, Security, Challenges, and ThreatsPreprint

Sumanth, N., Heggere, S. B., Prathiksha, E., Shetty, C. A., Nikhitha, H. R., & Soundarya, B. C. (2023) · Research Square · doi:10.21203/rs.3.rs-3223963/v1