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

Heterogeneous federated semantic segmentation

Zhang, C., Wang, J., Xie, Y., Wang, X., & Yu, B. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131470

Secure Energy Management for Ship Power Systems Using Federated Learning

Khan, A. A., Muriithi, G., Timilsina, L., Moghassemi, A., Papari, B., Edrington, C. S., Özkan, G., Boghrabadi, N. S., & Wang, Z. (2025) · IEEE Electric Ship Technologies Symposium (ESTS) · doi:10.1109/ests62818.2025.11152431

Optimized Federated Ensemble Learning for Secure IoMT-Based Healthcare Monitoring System

Ghosh, T., Patra, S. S., & Mondal, S. (2025) · International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) · doi:10.1109/isac364032.2025.11156921

Federated Learning for Enhanced Intrusion Detection: Combating Digital Deception in IoTEnabled Social Networks

Soni, S., Binjola, R., & Mittal, K. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898810030125040005

Integrating Federated Deep Learning and Blockchain for Privacy-Preserving Precision Medicine in Medical Virology

Desai, H. (2025) · Medical virology · doi:10.1007/978-981-96-8919-4_10

Robust Contribution Measurement for Federated Learning

Han, J. (2025) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat66811.2025.11181719

Federated Learning in Image Processing for Clothes Recognition

Gupta, M., Budhraja, H., Bhardwaj, N., Agarwal, L., Singh, R., & Chaturvedi, R. (2025) · Wiley eBooks · doi:10.1002/9781394167760.ch22

Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data

Lomasov, S., Fang, H., & Wang, H. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3761824

A federated meta-learning aided intelligent edge framework by using the parameter optimization approach

Xiaofeng, Z., Qiaosong, F., Peng, J., & Yuwen, Q. (2025) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-025-02511-7

A novel hybrid YOLO-O SegNet for object detection and optimization with DCNN-based object recognition in federated learning

Soomro, P. D., Fu, X., Banbhrani, S. K., Asad, M., & Shuaibu, Z. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-21131-3

Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks

Ziaeddini, A., Yakimenka, Y., & Kliewer, J. (2025) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit63088.2025.11195385

FREQSEL: A Frequency-Based Method for Client Selection in Federated Learning

M’Haouach, M., Houdou, A., Alami, H., Bouraqqadi, H., & Fardousse, K. (2025) · IEEE Access · doi:10.1109/access.2025.3624632

Merging Subgroup Information to Supplement Personal Information for Personalized Federated Learning Through Similar Client Grouping

Cai, X., & Zhou, W. (2025) · Computational Intelligence · doi:10.1111/coin.70135

Federated Learning for Healthcare: Analysis and Study on Heterogeneous Datasets using FedAP

Mehek, S. T., Reddy, C. K. K., Vanka, S., Alhameed, M. H., Shuaib, M., & Alam, S. (2025) · IEEE Congress on Information Science and Technology (CiSt) · doi:10.1109/cist65886.2025.11224123

Efficiency Boost in Decentralized Optimization: Reimagining Neighborhood Aggregation with Minimal Overhead

Kalwar, D., Baranwal, M., & Khadilkar, H. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761288

Privacy Enhancing Federated Learning for Predicting Energy Consumption in Smart Buildings

Sinaei, S., Mohammadi, M., Eklund, D., & Abrahamsson, H. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228194

FedETE: Privacy-Preserving Federated Event Trigger Extraction

Hu, F., Chang, T., Wu, M., Dong, S., Zhou, J., Yin, J., & Wang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_6

Enabling Trustworthy Recommendations in the Federated IoT: A Secure and Verifiable Tensor Learning Approach

Lu, R. X., Duan, X., Deng, G., & Ding, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3633684

Semi-decentralized federated learning with client pairing for efficient mutual knowledge transfer

Yang, D., Lee, J., & Choi, S. G. (2025) · Scientific Reports · doi:10.1038/s41598-025-29491-4

Knowledge Distillation and Tensor Decomposition-Based Privacy-Preserving Federated Learning for Industrial IoT Radar Sensing Systems

Wang, Y., Mu, J., Yao, Z., Wang, J., Ouyang, W., Zhou, Q., Xu, F., & Chen, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3635726

HCDFL with Information Optimization for Satellite Formation Fault Diagnosis

Du, X., Zhang, X., Liu, D., Liu, W., & Zong, Q. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.11.212

Detoxification of Poisoned Recognition Models by Fine-Tuning with Out-of-Distribution Samples

Takano, J., & Nakamura, K. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11248971

From Heterogeneous Data to Secure Convergence: A Data-Centric Review of Federated LearningPreprint

Nguyen, H. Q., Bettinelli, M., Ghaffari, A., Benoît, A., Nguyen, T., Pirttikangas, S., & Lovén, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5856890

Deploying Federated Learning in Healthcare: Techniques, Real-World Applications, and Challenges

Jaswal, R., Kaur, K., & Parihar, S. (2025) · doi:10.1109/cises66934.2025.11265630

SCOPE-FL: Seleção de Clientes por Ordem de Entropia

Silva, I. O., Vitelli, C., & Medeiros, I. (2025) · doi:10.5753/erin.2025.16155

Federated Learning using Deep Learning-Intrusion Detection system (FL DL-IDS) for IoT Applications

Bhavsar, M., Pradhan, S., Bekele, Y. B., & Dave, R. (2025) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme64568.2025.11277580

WoTtrader: A trading service for the Web of Things

Llopis, J. A., Iribarne, L., Criado, J., & Ayala, R. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101784

Blockchain-Enhanced Homomorphic Encryption for Neuro-Signal Driven Collaborative Intelligence in Secure Adaptive Learning Environments

Jerang, R., Mahato, G. K., Banerjee, A., & Chakraborty, S. K. (2025) · IETE Journal of Research · doi:10.1080/03772063.2025.2590756

Defendendo Modelos de Aprendizado de Máquina: Estratégias de Detecção de Anomalias Contra Ataques de Envenenamento

Hipolito, L., Lopes, A., Clipes, M., & Bastos, L. (2025) · doi:10.5753/erin.2025.16049

Federated Learning for Video Violence Detection: Complementary Roles of Lightweight CNNs and Vision-Language Models for Energy-Efficient Use

Thuau, S., Haidar, S., & Chelouah, R. (2025) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai66417.2025.00106

Towards Uncertainty-Aware Forecasting Using Tree-Based Aggregation in Federated Learning

Aupke, P., Zafar, M. A., Seema, Kassler, A., & Theocharis, A. D. (2025) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope64741.2025.11305284

FedEMMD: Entropy and MMD-based data and aggregation selection for non-iid and long-tailed data in federated learning

Saadat, N. G., & Santhosh Kumar, G. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102159

BC and Federated Learning for the Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds

Golash, R., Ahmad, S., Bhawana, & Kumar, N. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-96265-3_4

Federated Learning-Enhanced Brain Tumor Prediction on Edge Networks Using MRI Imaging

Doulani, K., & Sharma, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-3671-9_13

Semantic Communication-Assisted Cloud-Edge-Client Hierarchical Efficient Federated Learning

Yuan, P., & Tian, X. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323209

Reliable AI for Decision-Making and Resource Allocation in Integrated Terrestrial and Non-terrestrial Networks

Diamanti, M., Charatsaris, P., Stai, E., Zafeiropoulos, A., & Papavassiliou, S. (2026) · Synthesis lectures on communications · doi:10.1007/978-3-032-06512-4_4

Frugal Federated Learning for Violence Detection: A Comparison of LoRA-Tuned VLMs and Personalized CNNs

Thuau, S., Haidar, S., Bajracharya, A., & Chelouah, R. (2025) · International Conference on Federated Learning Technologies and Applications (FLTA) · doi:10.1109/flta67013.2025.11336730

Federated learning-based IoMT networks for improved performance of smart health

Öksüz, C., & Sağbaş, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36556-0.00010-8

SFI: A Practical and Efficient Backdoor Attack Framework Against Split Learning

Yu, F., Zeng, B., Zhao, K., Pang, Z., & Wang, L. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3654766

Blockchain-Governed Federated Intelligence: A Formal Model for Trust-Aware and Auditable Collaborative LearningPreprint

Hosseinalibeiki, H., Sepehrzad, R., & Heidari, M. (2026) · Research Square · doi:10.21203/rs.3.rs-8779446/v1

QS-FedNeRF: Quantized transmission and device selections federated neural radiance fields for edge intelligence

Li, X., Liu, Y., Zhang, X., Wu, G., & Fang, Y. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.106004

A review of malicious traffic detection for satellite-terrestrial integrated networks

Wan, M., Zhang, Z., Geng, L., Sun, J., Liu, Y., & Ma, W. (2026) · Computers & Security · doi:10.1016/j.cose.2026.104869

Federated Hybrid CNN Vision Transformer Framework for Breast Cancer Classification Under Simulated Non-IID Settings

Panda, B., Patro, B. S., Das, S. S., & Kar, S. K. (2026) · ASIDE Oncology · doi:10.71079/aside.onc.030926535

Performance Optimization of Adaptive Scheduling Mechanism in Pattern Recognition under Edge Federated Learning

Shen, Y. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426400045

Federated Learning for Cross-Cultural English Vocabulary Acquisition Under Non-IID and Privacy Constraints

Yan, N. (2026) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026826500082

Asynchronous Decentralized Federated Learning Deconstructs Excessively Large Batches

Wang, Z., Wang, F., Gursoy, M. C., & Velipasalar, S. (2025) · Globecom · doi:10.1109/globecom59602.2025.11432271

NeuDFL: Efficient Neuron-Based Defense Against Label Flipping Attacks on Non-IID Data

Fan, K., Wang, H., Li, W., Li, X., Li, H., Zhang, K., Yang, Y., & Wang, L. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3676632

Secure UAV-AV communications architecture in non-terrestrial networks: A review

Berini, A. D. E., Jamil, N., Benrazek, A., Nordin, R., Ibrahim, A. M., Awad, A. I., Shariq, M., Lakas, A., & Ferrag, M. A. (2026) · Vehicular Communications · doi:10.1016/j.vehcom.2026.101023

Federated Learning for Privacy-Preserving Credit Risk Assessment

Anerao, P. R., Prakash, O., Ikhar, S., Sankaran, N., Ashurali, A., & Bekbaulievich, A. I. (2025) · International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) · doi:10.1109/macs68476.2025.11453432

Multi-Agent Hybrid Deep Reinforcement Learning Approach for Resource Allocation in Multi-IRS-Assisted AAV-Aided V2X Communication

Joshi, N., Budhiraja, I., & Bansal, A. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3679519

Remote Rehabilitation Monitoring Using IoT and AI Techniques for Patients in Rural Areas

Alonso-Rollán, S., Carrera, A., & Chamoso, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-05748-8_48

Influence of Clustered Federated Learning in Enhancing the Lifetime of Edge based AQI Forecasting Networks

Kayalvizhi, R., & Durairaj, U. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11465651

Reliable and Communication-Efficient Federated Learning for Future Intelligent Edge Networks

Mestoukirdi, M. (2023) · theses.fr (ABES) · doi:10.70675/31490459z8643z4283z8172z1bc230436886

Secure and Efficient Federated Ensemble Learning for Hyperspectral Image Classification

Kumar, A., & Mahajan, S. (2026) · IETE Journal of Research · doi:10.1080/03772063.2026.2651258

FLAM: Federated Learning Based on Asynchronous Model Distillation

Tsuruta, Y., & Tamura, K. (2025) · IEEE International Workshop on Computational Intelligence and Applications (IWCIA) · doi:10.1109/iwcia68353.2025.11479775

Fine-grained Federated Traffic Prediction Model Based on Heterogeneous Pattern Representation Learning

Sun, S. Y., Chen, B., Jiang, X., Zhang, X., Song, Z., & Yang, L. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487341

Entropy-Regularized Weighted Federated Meta-Learning Approach for Power Load Forecasting in Non-IID Scenarios

Yang, Y., Zhu, F., Gao, Y., & Wang, L. (2026) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2026.3688250

One-Shot Federated Learning With Lightweight Intermediate Models in Wireless Sensor Network Setting

Dou, Y., Wang, H., Wan, J., Zhang, L., Huang, J., & Liu, Y. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3688766

Federated Learning for Alzheimer's Disease: Integrating Neuroimaging and Genomic Biomarkers Using Multi-Center MRI Data

Aggarwal, R., Bhavana, Dutta, N., & Aggarwal, E. (2026) · World Conference on Computational Science and Technology (WcCST) · doi:10.1109/wccst67302.2026.11495745

Data-Driven Precision Agriculture: Implementing Federated Learning for Improved Crop Management

Sameena, K., & Raja, D. R. K. (2026) · IEEE International Conference on Emerging Computing and Intelligent Technologies (ICoECIT) · doi:10.1109/icoecit68303.2026.11497388

End-to-End Secure Federated Learning for Cross-Client Credit Card Fraud Detection

Faulkner, F., Patra, A. N., Shukla, R. M., & Bhunia, S. (2026) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon66367.2026.11503474

Federated learning with context-aware client collaboration: Challenges, advances, and open problems

Ziaeinejad, S., & Samet, S. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104446

Enhancing Healthcare Data Privacy and Model Utility in Vertical Federated Learning Using Differential Privacy

Manna, S., Sarkar, A., & Bera, R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-17187-0_13

FedTabTran: A TabTransformer-Based FL Approach for Prediction of Cardiovascular Diseases

Hansda, S., Dasgupta, K., Banerjee, P., Das, D., Biswas, M., & Banerjee, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-17184-9_29

ProtoFed: Prototype-Enhanced Federated Meta-Learning for Few-Shot Rolling Bearing Fault Diagnosis

Jin, Y., Luo, Y., Liu, X. M., Fan, Y., & Shi, J. (2026) · Applied Sciences · doi:10.3390/app16115277

Analysis of Privacy-Preserving Federated Learning’s Resilience against Adverse Attack in Internet of Things Systems

Molose, R. R. S., Isong, B. E., Abu-Mahfouz, A. M. I., & Dladlu, N. (2025) · IEEE AFRICON · doi:10.1109/africon66545.2025.11533780

Data Privacy in Machine Learning: Challenges and Federated Learning Solutions

ÖZEN, G. Z., & Özen, Y. (2026) · Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi · doi:10.35193/bseufbd.1756289

Decentralized Federated Learning with Differential Privacy for ECG Classification

Jagathpally, A., Shahwar, T., & Kumar, P. M. (2026) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi68585.2026.11539857

Multi-Agent Paradigms for Privacy Preservation in Federated Learning: A Review

H.Bhole, R., & Sakhare, S. R. (2026) · International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst68392.2026.11545201

Federated learning at a glance

Mukherjee, A., Das, S. K., & Buyya, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-344433-3.00006-x

Centralized versus decentralized federated learning

Arévalo, I., & Salmeron, J. L. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-344433-3.00008-3

Multi-source perception early disease monitoring system based on IoT and large language models: research status, layered architecture and future trends

Wang, B., & Hua, X. (2026) · Cluster Computing · doi:10.1007/s10586-026-06141-6

Semi-synchronous federated learning in edge computing using a semi-decentralized topology

Hassanpour, M., & Ramezani, R. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08660-w

Uma Arquitetura de Co-orquestração SDN e Aprendizado Federado para Seleção Adaptativa de Clientes

de Oliveira, J. V., de Queiroga, C., & Matos, F. (2026) · doi:10.5753/wgrs.2026.24117

Federated Learning Vehicle Terminal Selection Method Based on Vehicle Service Capability in Internet of Vehicles

Li, Z., Liu, J., Luo, M., Li, L., & Dong, P. (2026) · Mechanisms and machine science · doi:10.1007/978-3-032-11165-4_88

A Survey on Security Threats and Mitigation Mechanisms for Smart Hospitals in the 6G Era

Maraziotis, O., Μαντάς, Γ., Rodriguez, J., & Gil‐Castiñeira, F. (2026) · Sensors · doi:10.3390/s26134304

Verification of Intelligent Collaborative Optimization Algorithm for Distributed Network Learning Based on Edge Computing

Zheng, X. B. (2026) · International Conference on AI, Data Science, Cyber Security and Smart Manufacturing for Sustainable Development (ICADCS) · doi:10.1109/icadcs70036.2026.11583033

CAEBA: A Dynamic Hidden Backdoor Attack Framework in Federated Learning

Guo, X., Li, G., & Hu, Y. (2026) · ACM Transactions on Privacy and Security · doi:10.1145/3831698

Ingeniería de software aplicada al aprendizaje federado: estudio de bibliotecas y mecanismos de protección de datos

Rosado, G. A. H. (2025) · CognitivaTech Ingeniería de Software Inteligente y Sistemas Adaptativos · doi:10.63688/cognitivatech.v2.i1.30

Mechanism Design for Federated Learning With Non-Monotonic Network Effects

Li, X., Luo, B., Huang, J., & Luo, Y. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3717853

Stress Monitoring with Machine Learning: Detecting and Managing Mental Health

Jiang, S., Li, Y., Firouzi, F., Chakrabarty, K., & Elbogen, E. B. (2025) · Springer eBooks · doi:10.1007/978-3-032-09253-3_4

Radio and Compute Resource Allocation for SWIPT and RIS-Assisted AirComp Federated Learning

Voikos, S., Charatsaris, P., Diamanti, M., Tsiropoulou, E. E., & Papavassiliou, S. (2026) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2026.3722443

FedIPSA: Personalized Federated Learning via Importance-Based Parameter Masking and Selective Alignment

Vo, A.-K., Huynh‐The, T., & Kim, T. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3725871

Decentralized Knowledge Distillation for Remaining Useful Life Prediction: A Case Study on Turbofan Jet Engine Degradation

Molo, M. J., Vadicamo, L., Carlini, E., Gennaro, C., Kambale, W. V., & Kyamakya, K. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00030

Collaborative fuzzy rule-based modeling with a multi-source data environment

Shen, Y., Wang, B., Hu, X., Pedrycz, W., Chen, Y., & Liu, B. (2026) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2026.110097

RAD-SFL: Resource-Aware Dynamic Split Federated Learning for Heterogeneous Edge Environments with Non-IID Data

Kalapaaking, A. P., Stephanie, V., Samikwa, E., Khalil, I., & Braun, T. I. (2026) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge72783.2026.00026

FedQS: asynchronous federated learning based on queue scheduling

Zhou, J., Li, F., Shi, T., & Quan, X. (2026) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-026-00977-4

Scenario-Based Federated Learning for Privacy-Aware IoT Botnet Intrusion Detection

Shafei, H. A. (2026) · IEEE International Conference on Artificial Intelligence Implementation & Applications (ICAIIA) · doi:10.1109/icaiia70172.2026.11691741

Leveraging federated learning for smart grids: A comprehensive survey

Husnoo, M. A., Anwar, A., & Rahman, M. A. (2026) · Cyber-Physical Energy Systems · doi:10.1016/j.cpes.2026.07.002

Federated learning-enabled analytics for ESG data authenticity in digital apparel ecosystems: A platform governance perspective

Chen, W., Ouyang, Z., Fu, Y., Zhong, R. Y., Yi, Z., & Cai, Y. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.112382

Bias Mitigation in Federated Learning through Feature Integration

Zhang, Z., Ouyang, M., Li, K., Chen, H.-R., Yang, L., & Lin, W. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3848122

Federated Learning-Based Regression Framework to Predict Printed Line Width in Electrohydrodynamic Inkjet Printing

Zhang, P., Yun, J., & Qin, H. (2026) · Manufacturing Letters · doi:10.1016/j.mfglet.2026.08.036

Decentralized intelligence in smart grids: a comprehensive survey of edge computing and federated learning

Pramanik, P. K. D., Upadhyaya, B. K., Kushwaha, A., & Bhowmik, D. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100925

Asynchronous federated learning based on orthogonal correction and bias compensation

Chen, H., Chen, X., An, Y., Ren, L., Qin, Y., & Feng, H. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134721

Attention-based and contrastive deep learning models for real-time anomaly detection in Edge-IIoT networks

Alzighaibi, A. R., Marks, A., Almudaifer, A. I., Balaha, H. M., Halima, N. B., & Elhosseini, M. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-70891-x

A systematic review of federated learning based intrusion detection systems in software defined networking

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