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

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

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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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Mitigating the impact of data dependence in machine-learning-based state-of-health estimation for lithium-ion batteries: Challenges, strategies and perspectives

Jiang, M., Wang, M., Liu, M., Gao, F., & Su, Y. (2026) · eTransportation · doi:10.1016/j.etran.2026.100606 · Cited by 1

DuaFed: A clustered federated learning framework via dual-domain feature alignment for tackling data heterogeneity

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From data heterogeneity to convergence: A data-centric review of federated learning

Nguyen, H. T., Bettinelli, M., Ghaffari, A., Benoît, A., Nguyen, T., Pirttikangas, S., & Lovén, L. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135005 · Cited by 1

FedA2W: adaptive aggregation with weighted loss proximity for non-IID federated learning

Jain, S., Gupta, S., Pundir, A., Singh, S., & Saxena, G. J. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12293-8 · Cited by 1

A multi-level taxonomy-driven survey of security threats and defenses in federated learning across silos and devices

Ezzeddine, H., Issa, R., & Abbas, N. (2026) · Journal of Cyber Security Technology · doi:10.1080/23742917.2026.2696259 · Cited by 1

Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning

Herath, K., Zhao, J. Y., & Bagchi, S. (2026) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn69566.2026.00032 · Cited by 1

A Fairness Perspective on Client Selection and Aggregation Methods for Non-IID Mitigation in Federated Learning: A Survey

Alsofyani, M., Al-Turaiki, I. M., & Mathkour, H. I. (2026) · Electronics · doi:10.3390/electronics15143178 · Cited by 1

A hierarchical federated learning framework with FedNova, game-theoretic matching, and QKD-assisted privacy for the internet of vehicles

Vinita, L. J., & Vetriselvi, V. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1861374 · Cited by 1

Federated learning-based optimization for secure and efficient microgrid energy management with peer-to-peer trading, adaptive demand response, and grid stability enhancement

Qiao, T., Zhang, L., Wang, Z., Wang, C., & Fan, J. (2026) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2026.102485 · Cited by 1

A review of federated learning: architectures, challenges, and targeted solutions

Velaei, M., Ghahramani, H., Ghaffari, A., & Derakhshanfard, N. (2026) · Cluster Computing · doi:10.1007/s10586-026-06286-4 · Cited by 1

Enhancing the manufacturing processes and productivity in Industry 5.0 with the integration of machine learning and cyber-physical systems

Naik, V., Das, S., Vinay, M. N., Manikandan, H., & Kumar, K. M. (2026) · International Journal of Intelligent Unmanned Systems · doi:10.1108/ijius-05-2026-0176 · Cited by 1

A Systematic Analysis of Federated Learning

Chaudhari, A. N., Dr.Hitham Seddig, A. A., Sarlan, A. B., & Raut, R. (2022) · International Conference on Digital Transformation and Intelligence (ICDI) · doi:10.1109/icdi57181.2022.10007356 · Cited by 1

Federated Grouping Panoptic Segmentation for LiDAR Point Cloud Based on Convolutional Network

Shi, C., Own, C., & Zhou, R. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00010 · Cited by 1

NeuralMatch: Identificando a Similaridade de Clientes baseado em Modelos no Aprendizado Federado

Talasso, G. U., Souza, A. M. D., & Villas, L. A. (2023) · Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC) · doi:10.5753/sbrc_estendido.2023.808 · Cited by 1

A Hybrid Federated Reinforcement Learning Approach for Networked Robots

Rangu, G., Kulkarni, D. D., Nair, J. S., & Nair, S. B. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-4713-3_47 · Cited by 1

A Distributed Framework for the Ocean IoT Network

Ning, J., Wang, J., Feng, P., & Yang, T. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10294049 · Cited by 1

A Distributed Training Fault Diagnosis Method for Big Data in Rail Transit System

He, Q., Cai, Y., & Wen, T. (2023) · CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) · doi:10.1109/safeprocess58597.2023.10295834 · Cited by 1

Benchmarking Client Selection Methods in Non-IID Federated Learning Scenarios

M’Haouach, M., Choukhairi, M., & Fardousse, K. (2023) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom59760.2023.10322914 · Cited by 1

Toward Delay-Conscious Federated Learning: A Systems Perspective

Gedawy, H. K., Harras, K. A., & Erbad, A. (2023) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2200196 · Cited by 1

Joint Learning: A Pattern for Reliable and Efficient Decision-Making in Self-Adaptive Internet of Things

Provoost, M., Weyns, D., Landuyt, D. V., Michiels, S., & Bureš, T. (2023) · European Conference on Pattern Languages of Programs · doi:10.1145/3628034.3628043 · Cited by 1

DTPP-DFL: A Dropout-Tolerated Privacy-Preserving Decentralized Federated Learning Framework

Chen, T., Wang, X., Dai, H., Yang, H., Zhou, R., & Zhang, X. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437934 · Cited by 1

An Analysis of Federated Learning Performance in Mobile Networks

Bochie, K., Sammarco, M., & Campista, M. E. M. (2023) · IEEE Virtual Conference on Communications (VCC) · doi:10.1109/vcc60689.2023.10474642 · Cited by 1

Enhancing Security and Efficiency: A Lightweight Federated Learning Approach

Chen, C., Wang, K. I., Li, P., & Sakurai, K. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-57916-5_30 · Cited by 1

Federated Learning and Convolutional Neural Networks for Intrusion Detection Systems

Abid, M., Benkaddour, M. K., & Benouis, M. (2024) · International Conference on Pattern Analysis and Intelligent Systems (PAIS) · doi:10.1109/pais62114.2024.10541284 · Cited by 1

FedSCD: Federated Learning with Semi-centralization, Discrepancy-Awareness and Dual-Model Collaboration

Wang, C., Pan, C., Gan, Q., Liu, Z., Liu, N., & Zhang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5101-3_10 · Cited by 1

FedLRDP: Federated Learning Framework with Local Random Differential Privacy

Zhou, R., Dong, A., Yu, J., & Ding, Q. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650657 · Cited by 1

Decentralized Federated Learning Links for Biometric Recognition

Guo, J., Mu, H., Ren, H., Han, C., & Sun, L. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650816 · Cited by 1

EncGradInversion: Image Encoding and Gradient-Inversion-Based Batch Attack in Federated Learning

Dao, T.-N., & Lee, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3483850 · Cited by 1

DFedMD: Decentralized Federated Learning using Model Distillation

Abiru, S., & Tamura, K. (2024) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scisisis61014.2024.10759998 · Cited by 1

Combining GANs and Federated Learning for Privacy Protection

Cozuc, A., Oltean, A., Mocanu, I. G., & Cramariuc, O. T. (2024) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb64556.2024.10805604 · Cited by 1

Efficient Federated Learning Via Low-Rank Gradient Compression for Intelligent Transportation System

Li, Q., Ma, X., Xiao, T., Zhu, Y., & Cai, R. (2024) · Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC) · doi:10.1109/csrswtc64338.2024.10811557 · Cited by 1

Docker Survey for FLOps Efficiency

Staňo, M., Hluchý, L., Krammer, P. H., & Hučko, M. (2025) · Cybernetics & Informatics (K&I) · doi:10.1109/ki64036.2025.10916451 · Cited by 1

eFLEET: a framework in federated learning for enhanced electric transportation

de Gauna, D. E. R., Ruiz‐Iniesta, A., & Crespo, L. E. S. (2025) · International Journal of Web Information Systems · doi:10.1108/ijwis-09-2024-0281 · Cited by 1

Accelerating Federated Learning with Adaptive Test-Driven Quantization at Edge Network

Liu, Y., Fan, C., & Zhang, X. (2024) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct62411.2024.10946551 · Cited by 1

Experimental analysis of data protection, efficiency and effectiveness of federated learning methods

Gedikoglu, F., & Bök, P.-B. (2024) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc63386.2024.00045 · Cited by 1

Federated Learning for Secure and Privacy-Preserving Medical Diagnostics

Gupta, G., Kant, R., & Adem, A. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2827-0.ch006 · Cited by 2

Federated learning for distributed intrusion detection in IoT networks

Du, Q., Li, B., Shao, Z., Wu, Y., & Yang, C. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-329032-9.00009-9 · Cited by 2

Graph-Relational Federated Learning: Enhanced Personalization and Robustness

Lin, W., Lan, H., He, H., & Li, B. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3574329 · Cited by 1

Energy-Efficient Federated Learning for AIoT Using Clustering Methods

Pereira, R., Famá, F., Kalalas, C., & Dini, P. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3579583 · Cited by 1

Federated Learning

Kobayashi, M. (2025) · Springer eBooks · doi:10.1007/978-981-96-9223-1_4 · Cited by 1

Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection

Nogami, K., Tamura, H., & Tanaka, G. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228565 · Cited by 1

A Lightweight Central Learning Approach for Arrhythmia Detection from ECG Signals

Aboumadi, A., Yaacoub, E. E., & Abualsaud, K. (2021) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00021 · Cited by 1

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Almadhor, A. S., Khan, A. A., Wechtaisong, C., Yousaf, I., Kryvinska, N., Tariq, U., & Chikha, H. B. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.039007 · Cited by 1

Two Timescale Hybrid Federated Learning with Cooperative D2D Local Model Aggregations.Preprint

Lin, F. P.-C., Hosseinalipour, S., Azam, S. S., Brinton, C. G., & Michelusi, N. (2021) · arXiv (Cornell University) · Cited by 1

Distributed Network Traffic Classification Based on Heterogeneous Federated EnsemblePreprint

Deng, Q., Liao, N., & Deng, Q. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4820781 · Cited by 1

Navigating Bias and Ensuring Fairness in Federated Learning

Vajrobol, V., Aggarwal, N., Baranwal, P., Saxena, G. J., Pundir, A., & Singh, S. (2025) · Wiley eBooks · doi:10.1002/9781394271382.ch11 · Cited by 1

Machine learning-Enabled Network Traffic Analysis

Aouedi, O. (2022) · theses.fr (ABES) · doi:10.70675/98b92b52z17b2z43b7zb42ez26f74d2fff67 · Cited by 1

Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems

Dirir, A. M., Salah, K., & Svetinović, D. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_7

Intelligent Bilateral Client Selection in Federated Learning Using Game Theory. (c2022)

Wehbi, O. (2022) · doi:10.26756/th.2022.451

Federated Machine Learning for Multi-Aspect Neuro-developmental Disorders

Shamseddine, H. (2022) · doi:10.26756/th.2022.508

A Mathematical Theory of Knowledge for Intelligent AgentsPreprint

Ekolle, Z. E., Kohno, R., & Ochiai, H. (2022) · Preprints.org · doi:10.20944/preprints202211.0450.v1

Evaluating distributed-learning algorithms on real-world healthcare dataPreprint

Coutinho‐Almeida, J., Cruz-Correia, R. J., & Rodrigues, P. (2022) · Research Square · doi:10.21203/rs.3.rs-2292004/v1

Utilization of synthetic system intelligence as a new industrial asset

Horváth, I. (2023) · Journal of Integrated Design and Process Science · doi:10.3233/jid-220024

Towards dependable 6G networksPreprint

Rodriguez, F., Ahmad, I., Huusko, J., & Seppänen, K. (2023) · doi:10.36227/techrxiv.20237358.v2

A pipeline to collaborative AI models creation between Brazilian governmental institutions

de Souza, G. A. F., Figueredo, M., Sabino, D., & Cacho, N. (2023) · International Symposium on Autonomous Decentralized Systems · doi:10.1109/isads56919.2023.10092030

FedSensor: framework de aprendizagem federada voltado para a eficiência energética e segurança de dispositivos IoT ultra-restritos.

Ferraz, N. (2022) · doi:10.11606/t.3.2022.tde-04052023-092320

A Study of Blockchain-Assisted Two-Layer Asynchronous Federated Learning in IovsPreprint

Wang, L., Wu, S., Yang, N., & Huang, C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4484225

Federated Learning-Based Interference Modeling for Vehicular Dynamic Spectrum Access

Hoffmann, M., Kryszkiewicz, P., & Kliks, A. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-34776-4_23

CAIM: A Context-Aware Incentive Mechanism for Robust Federated Learning

Chang, C.-W., Cui, Y., Shen, J., & Wei, T. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00032

Fair Rewards in Federated Learning: A Novel Approach with Adjusted OR-TMC Shapley Value Approximation Algorithm

(2023) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2023/081242023

Spectrum Occupancy Detection Supported by Federated Learning

Kułacz, Ł. (2023) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom58365.2023.10271668

Towards Boosting Federated Learning Convergence: A Computation Offloading & Clustering Approach

AbdulRahman, S., Bouachir, O., Otoum, S., & Mourad, A. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279581

Privacy-preserving Federated Learning Against Byzantine Attack via Top-k Indexes

Shang, M., Tong, Z., Liu, T., & Zhang, T. (2023) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana60121.2023.00108

Data sharing or algorithm sharing?Preprint

Smichowski, B. C., Lefouili, Y., Mantovani, A., & Reggiani, C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4641819

Privacy-Preserved Collaborative Federated Learning Platform for Industrial Internet of Things

Pathiraja, L., Lakshan, I., Kushani, K., Sandeepa, C., Gamage, T. D., Weerasinghe, T. N., & Liyanage, M. (2023) · IEEE Latin-American Conference on Communications · doi:10.1109/latincom59467.2023.10361886

Using Virtualization Approaches to Solve Deep Learning Problems in Voluntary Distributed Computing Projects

Kurochkin, I. I., & Papanov, V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49435-2_6

Enhancing Central Model Performance: Leveraging Federated Learning Across Virtual Machine Networks for Distributed Training and Synchronization

Virwani, R., & Bhattacharya, S. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-15478

A New Methodology for Edge Intelligence Data Quality Evaluation in Idd and Non-Iid Dataset in Federated LearningPreprint

Neto, E. G. V., Peixoto, S. A., Leithardt, V. R. Q., dos Anjos, J. C. S., & Paz, J. F. D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4821752

Taxonomy for federated learning in digital healthcare systems

Udeji, F., Sur, S. N., Kumaravelu, V. B., & Kavitha, K. V. N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00008-4

The roadmap towards an overarching data sharing infrastructure at Wageningen University & Research : Smart infrastructures for farm-generated data towards circular agriculture

Bulens, J. D., Bogaardt, M. J., Lokers, R. M., Kamphuis, C., & Sijbrandi, F. (2024) · doi:10.18174/659257

Federated Learning assisted framework to periodically identify user communities in urban space

de Souza, C. D. T., de Rezende, J. F., & Campos, C. A. V. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103589

A Review of Privacy-Preserving Decentralized Learning

Gitanshu, & Kumar, N. B. M. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2839-8_29

Soluções para Dados Heterogêneos em Aprendizado Federado através de Similaridade de Modelos e Agrupamento de Clientes

Talasso, G. U., & Villas, L. A. (2024) · Revista Eletrônica de Iniciação Científica em Computação · doi:10.5753/reic.2024.4649

Exploring Federated Learning: The Framework, Applications, Security & Privacy

Saha, A., Ali, L., Rahman, R., Monir, M. F., & Ahmed, T. (2024) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom61746.2024.10646291

A Diagnosis Model Based on Federated Learning for Lung Cancer Classification

Babu, A. M., & Bellamkonda, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-72284-4_13

ROZPROSZONE WYKRYWANIE ZAJĘTOŚCI WIDMA OPARTE NA UCZENIU FEDERACYJNYM

Kułacz, Ł. (2024) · Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne · doi:10.15199/59.2024.4.10

FedJS: An Efficient Federated Learning Approach for the Internet of Vehicles

Ye, T. (2024) · International Symposium on Computer and Information Processing Technology (ISCIPT) · doi:10.1109/iscipt61983.2024.10672672

FedUNA: A Federated Learning Approach for Robust and Privacy-Preserving Pothole Classification using EfficientNet

Khan, M. N., Mohd, M. N. H., Khan, F. S., Rahman, T. A., Sheikh, U. U., & Bakht, M. P. (2024) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa62061.2024.10686750

An Efficient SVM-Based Method for Client Access Permission Distribution in Federated Learning

Yu, X., Cui, Y., & Zheng, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5552-3_30

Artificial intelligence in healthcare

Canelas‐Pais, M., Coutinho‐Almeida, J., Araujo, S. M., Rafael, F. M., Cruz-Correia, R. J., & Rodrigues, P. P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91819-0.00009-9

Byzantine-Robust Federated Learning Based on Blockchain

Song, L., Cai, C., Wei, S., Banerjee, R., Feng, X., & Jiang, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71464-1_39

Exploring 6G Research

Kumar, S. S., Balaji, S., Devi, S. N., & Priyadharsini, V. (2024) · Wiley eBooks · doi:10.1002/9781394230686.ch16

Paradigm shift from machine learning to federated learning

Vaithiyanathan, R., & Ranjini, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28951-4.00010-1

DLShield: A Defense Approach Against Dirty Label Attacks in Heterogeneous Federated Learning

Sameera, K. M., Abhinav, M., Amal, P. P., Abhiram, T., Abishek, R. K., Amal, T., Anaina, P., Vinod, P., A., R. R. K., & Conti, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-80408-3_9

FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution

Chen, Z., Chen, H., Liu, S., & Liu, Y. (2024) · International Conference on Algorithms, Data Mining, and Information Technology (ADMIT) · doi:10.1145/3701100.3701248

Federated Learning Approaches for Anomaly Detection in Distributed Network Environments

Deepika, P., Jyotsna, T., Surekha, G., Pratyusha, T., Usha, R., & Praveena, G. L. (2025) · Cognitive science and technology · doi:10.1007/978-981-97-9266-5_50

FeDMus: Federated Dynamic Example Mining for Unlabeled Sensor Data in Industrial Automation

Yang, W., Chen, H., Schlagenhauf, T., Li, Z., & Zou, Z. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3547220

An Insight into Federated Learning: A Collaborative Approach for Machine Learning

Sharma, P., Kashniyal, J., & ESham, E. (2024) · International Conference on Advancement in Electronics & Communication Engineering (AECE) · doi:10.1109/aece62803.2024.10911342

Real-Time Incremental Federated Learning for Weather Prediction with UAV based Data Collection

Singhal, P., Hazra, A., & Joshi, P. (2024) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves61986.2024.10927919

Dynamic Client Selection and Group-Balanced Personalization for Data-Imbalanced Federated Speech Recognition

Xu, C., Wu, Z., Ge, F., & Zhi, Y. (2025) · Electronics · doi:10.3390/electronics14071485

YOLO Meets FedAVG: A Privacy-Preserving Approach to Autonomous Vehicles Object Detection

Alahdal, N. M., Abukhodair, F., Meftah, L. H., & Cherif, A. (2025) · International Conference on Advanced Innovations in Smart Cities (ICAISC) · doi:10.1109/icaisc64594.2025.10959368

Introduction to Federated Intelligent Systems in Healthcare

Ahmad, N. S. (2025) · Wiley eBooks · doi:10.1002/9781394271382.ch1

K‐Anonymization and Residual Neuron Attention Network for Privacy Data Protection in Blockchain Network With Federated Learning Using Defense Application

Premkumar, T., & Krithika, D. R. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70182

Adding Data Quality to Federated Learning Performance Improvement

Neto, E. G. V., Peixoto, S. A., Leithardt, V. R. Q., Paz, J. F. D., & dos Anjos, J. C. S. (2025) · IEEE Access · doi:10.1109/access.2025.3578301

Security and Privacy in Federated Process Mining

Shaikh, E., Mohammad, N., Tariq, Z., & McClean, S. I. (2024) · IEEE Consumer Life Tech (ICLT) · doi:10.1109/iclt63507.2024.11038637

Federated Learning and MEC for Disaggregated RAN Monitoring in the 5G Non-Terrestrial Networks

Tsegaye, H. B., Tshakwanda, P. M., Worku, Y. M., Devetsikiotis, M., Sacchi, C., & Christodoulou, C. (2025) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero63441.2025.11068449

Heterogeneous Federated Semantic SegmentationPreprint

Bin, Y., Wang, J., Xie, Y., Wang, X., & Zhang, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5351405

Optimized Association Rule Mining for Predicting Heart Diseases from Distributed Medical Databases

Khatoon, S., & Pandey, S. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11070429

Optimization of VGG-16 Model Using Stripe-Wise Pruning in a Federated Environment

Hallad, S. S., Nadaf, S. M., Shetty, P., Bafna, A., Habib, K. V., Meena, S. M., & Kulkarni, U. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3652-5_5

Efficient Energy Theft Detection Utilizing Hierarchical Federated Learning

Shahid, S. S., Salman, T., Baza, M., & Srivastava, G. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106795

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