A Review of Privacy-Preserving Federated Learning for the Internet-of-Things

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Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond

Berkani, M. R. A., Chouchane, A., Himeur, Y., Ouamane, A., Miniaoui, S., Atalla, S., Mansoor, W., & Al-Ahmad, H. (2025) · Computers · doi:10.3390/computers14040124 · Cited by 63 · see citing works

Exploring privacy measurement in federated learning

Jagarlamudi, G. K., Yazdinejad, A., Parizi, R. M., & Pouriyeh, S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-023-05846-4 · Cited by 52 · see citing works

Federated Learning for Condition Monitoring of Industrial Processes: A Review on Fault Diagnosis Methods, Challenges, and Prospects

Berghout, T., Benbouzid, M., Bentrcia, T., Lim, W. H., & Amirat, Y. (2022) · Electronics · doi:10.3390/electronics12010158 · Cited by 50 · see citing works

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices

Alam, M. U., & Rahmani, R. (2023) · Sensors · doi:10.3390/s23020970 · Cited by 48 · see citing works

A systematic review of federated learning from clients’ perspective: challenges and solutions

Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10563-8 · Cited by 52 · see citing works

ML-MCU: A Framework to Train ML Classifiers on MCU-Based IoT Edge Devices

Sudharsan, B., Breslin, J. G., & Ali, M. I. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3098166 · Cited by 50 · see citing works

Blockchain-Based Privacy-Enhancing Federated Learning in Smart Healthcare: A Survey

Limbepe, Z. N., Gai, K., & Yu, J. (2025) · Blockchains · doi:10.3390/blockchains3010001 · Cited by 48 · see citing works

Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0

Peyvandi, A., Majidi, B., Peyvandi, S., & Patra, J. C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12900-5 · Cited by 43 · see citing works

Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches

Ahmed, I., Syed, M. A., Maaruf, M., & Khalid, M. (2024) · Computing · doi:10.1007/s00607-024-01356-0 · Cited by 40 · see citing works

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Alam, M. U., & Rahmani, R. (2021) · Sensors · doi:10.3390/s21155025 · Cited by 32 · see citing works

Empowering Dataspace 4.0: Unveiling Promise of Decentralized Data-Sharing

Alsamhi, S. H., Hawbani, A., Kumar, S., Timilsina, M., Al-Qatf, M., Haque, R., Nashwan, F., Zhao, L., & Curry, E. (2024) · IEEE Access · doi:10.1109/access.2024.3442968 · Cited by 30 · see citing works

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3055679 · Cited by 25 · see citing works

Memristor-Based Variation-Enabled Differentially Private Learning Systems for Edge Computing in IoT

Fu, J., Liao, Z., Liu, J., Smith, S. C., & Wang, J. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3023623 · Cited by 25 · see citing works

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

Mishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Cluster Computing · doi:10.1007/s10586-022-03813-x · Cited by 25 · see citing works

Federated learning‐based IoT: A systematic literature review

Hosseinzadeh, M., Hemmati, A., & Rahmani, A. M. (2022) · International Journal of Communication Systems · doi:10.1002/dac.5185 · Cited by 24 · see citing works

Blockchain-Enabled Federated Reinforcement Learning (B-FRL) Model for Privacy Preservation Service in IoT Systems

Alam, T., Gupta, R., Ullah, A., & Qamar, S. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-11411-w · Cited by 22 · see citing works

Open problems and challenges in federated learning for IoT: A comprehensive review and strategic guide

Diba, B. S., Plabon, J. D., Mowla, T. J., Nahar, N., Mistry, D., Sarker, S., Mridha, M. F., & Shin, J. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110515 · Cited by 21 · see citing works

Gain Without Pain: Offsetting DP-Injected Noises Stealthily in Cross-Device Federated Learning

Yang, W., Zhou, Y., Hu, M., Wu, D., Zheng, X., Wang, J. H., Guo, S., & Li, C. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3102030 · Cited by 22 · see citing works

Privacy-Preserving Machine Learning in Cloud–Edge–End Collaborative Environments

Yang, W., Wang, H., Li, Z., Niu, Z., Wu, L., Wei, X., Su, Y., & Susilo, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3461410 · Cited by 19 · see citing works

Understanding global aggregation and optimization of federated learning

Nanayakkara, S. I., Pokhrel, S. R., & Li, G. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.009 · Cited by 18 · see citing works

Federated learning for energy constrained devices: a systematic mapping study

Mokadem, R. E., Maissa, Y. B., & Akkaoui, Z. E. (2022) · Cluster Computing · doi:10.1007/s10586-022-03763-4 · Cited by 17 · see citing works

Federated Learning in IoT Environments: Examining the Three-Way See-Saw for Privacy, Model-Performance, and Network-Efficiency

Laidi, R., Merabtine, N., Djenouri, D., Latif, S., Qadir, H. A., Djenouri, Y., & Balasingham, I. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3557475 · Cited by 16 · see citing works

Using Domain-Augmented Federated Learning to Model Thermostatically Controlled Loads

Bálint, A., Raja, H., Driesen, J., & Kazmi, H. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3243467 · Cited by 13 · see citing works

MFLCES: Multi-Level Federated Edge Learning Algorithm Based on Client and Edge Server Selection

Liu, Z., Duan, S., Wang, S., Liu, Y., & Li, X. (2023) · Electronics · doi:10.3390/electronics12122689 · Cited by 13 · see citing works

Optimizing DNN training with pipeline model parallelism for enhanced performance in embedded systems

Maruf, M. A., Azim, A., Auluck, N., & Sahi, M. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104890 · Cited by 13 · see citing works

Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches

Pinto, G. P., & Prazeres, C. V. S. (2025) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp5020025 · Cited by 13 · see citing works

Dynamic computation offloading for ground and flying robots: Taxonomy, state of art, and future directions

Chaâri, R., Cheikhrouhou, O., Koubâa, A., Youssef, H., & Gia, T. N. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100488 · Cited by 12 · see citing works

NOSnoop: An Effective Collaborative Meta-Learning Scheme Against Property Inference Attack

Ma, X., Li, B., Jiang, Q. G., Chen, Y., Gao, S., & Ma, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3112737 · Cited by 11 · see citing works

Copyright protection framework for federated learning models against collusion attacks

Luo, Y., Li, Y., Qin, S., Fu, Q., & Liu, J. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121161 · Cited by 10 · see citing works

Efficient and Secure Aggregation Framework for Federated-Learning-Based Spectrum Sharing

Li, W.-W., Chen, G.-R., Zhang, X., Wang, N., Ouyang, D., & Chen, C.-X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3357575 · Cited by 9 · see citing works

Harnessing Federated Learning for Digital Forensics in IoT: A Survey and Introduction to the IoT-LF Framework

Mohamed, H., Koroniotis, N., Moustafa, N., Schiliro, F., & Zomaya, A. Y. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3492919 · Cited by 10 · see citing works

A survey of joint security-safety for function, information and human in industry 5.0

Hong, Y., Wu, J., & Guan, X. (2024) · Security and Safety · doi:10.1051/sands/2024014 · Cited by 11 · see citing works

Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts

Stephan, T., Paramana, P. P. D., Lin, C., Agarwal, S., & Verma, R. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01063-3 · Cited by 8 · see citing works

Tactile internet of federated things: Toward fine-grained design of FL-based architecture to meet TIoT demands

Alnajar, O., & Barnawi, A. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109712 · Cited by 8 · see citing works

Client scheduling and bandwidth slicing for multiple federated learning tasks over multiple passive optical networks

Bi, M., Yan, C., Lin, R., Hong, Y., & Li, J. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110309 · Cited by 7 · see citing works

Artificial Intelligence-driven privacy preservation in the internet of vehicles: a comprehensive systematic literature review

Heidari, A., Rastegar, S. H., & Khonsari, A. (2026) · Journal Of Big Data · doi:10.1186/s40537-025-01360-x · Cited by 7 · see citing works

Distributed Split Learning for Map-Based Signal Strength Prediction Empowered by Deep Vision Transformer

Yu, H., She, C., Yue, C., Hou, Z., Rogers, P., Vucetic, B. S., & Li, Y. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3320643 · Cited by 7 · see citing works

Federated cross-view e-commerce recommendation based on feature rescaling

Li, R., Shu, Y., Cao, Y., Luo, Y., Zuo, Q., Wu, X., Yu, J., & Zhang, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-81278-1 · Cited by 9 · see citing works

Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review

Peng, J., Guo, J., Bao, F., Yang, C., Xu, Y., & Qin, Y. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.041897 · Cited by 6 · see citing works

SunBlock: Cloudless Protection for IoT Systems

Safronov, V., Mandalari, A. M., Dubois, D. J., Choffnes, D., & Haddadi, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56252-5_15 · Cited by 6 · see citing works

Privacy preservation using optimized Federated Learning: A critical survey

Narule, Y. S., & Thakre, K. S. (2024) · Intelligent Decision Technologies · doi:10.3233/idt-230104 · Cited by 5 · see citing works

A federated data fusion-based prognostic model for applications with multi-stream incomplete signals

Arabi, M., & Fang, X. (2024) · IISE Transactions · doi:10.1080/24725854.2024.2360619 · Cited by 5 · see citing works

Federated Learning-based Routing Vulnerability Analysis and Attack Detection for Healthcare 4.0

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A Security-Oriented Overview of Federated Learning Utilizing Layered Reference Model

Lu, J., Fukumoto, N., & Nakao, A. (2024) · IEEE Access · doi:10.1109/access.2024.3404948 · Cited by 5 · see citing works

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 · see citing works

Privacy-Preserving Federated Machine Learning Techniques

Subramaniam, G., & Palanisamy, S. (2023) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-0593-5.ch007 · Cited by 4 · see citing works

A Comparative Study Between Solutions Proposed to Secure IoT Networks

Bachar, M., Khiat, A., & Bahnasse, A. (2025) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc66714.2025.11135134 · Cited by 4 · see citing works

A systematic review on privacy preservation in federated learning

Sum, A. S. I., Pritee, Z. T., Saha, A. K., Mahmud, M. A., & Nur, K. M. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01229-x · Cited by 6 · see citing works

Model-Heterogeneous Federated Learning With Bidirectional Knowledge Distillation

Zhang, H., Zhu, Y., Wu, T., Cheng, S., & Liu, J. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3599315 · Cited by 4 · see citing works

Federated Learning Approaches to Diverse Machine Learning Model: A Review

Sharma, S., & Kumar, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3761-5_25 · Cited by 4 · see citing works

Federated Learning for Internet of Things

Li, Y., Zhang, Q., Wang, X., Zeng, R., Li, H., Murturi, I., Dustdar, S., & Huang, M. (2023) · Springer eBooks · doi:10.1007/978-3-031-50514-0_3 · Cited by 5 · see citing works

Federated Learning for Privacy-Preserving Machine Learning in IoT Networks

Anitha, G., & Jegatheesan, A. (2024) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici62503.2024.10696723 · Cited by 4 · see citing works

Privacy-Enhancing Technologies for Federated Learning

Batool, Z., Buyukates, B., Nourmohammadi, R., & Zhang, K. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-78841-3_7 · Cited by 3 · see citing works

Recent Applications of Federated Learning in Edge and IoT Environments: A Review

Diván, M. J., Reynoso, M. L. S., & Gonnet, S. (2021) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon52037.2021.9702466 · Cited by 3 · see citing works

A Communication-Efficient Local Differentially Private Algorithm in Federated Optimization

Alam, S. E., Shukla, D., & Rao, S. (2023) · IEEE Access · doi:10.1109/access.2023.3283503 · Cited by 3 · see citing works

Privacy-Preserving Federated Learning

Kim, K., & Tanuwidjaja, H. C. (2021) · SpringerBriefs on cyber security systems and networks · doi:10.1007/978-981-16-3764-3_5 · Cited by 3 · see citing works

A Set of Tools and Data Management Framework for the IoT–Edge–Cloud Continuum

Judvaitis, J., Blumbergs, E., Arzovs, A., Mackus, A. I., Balašs, R., & Selavo, L. (2024) · Applied System Innovation · doi:10.3390/asi7060130 · Cited by 2 · see citing works

Network-aware federated neural architecture search

Öcal, G., & Özgövde, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.053 · Cited by 2 · see citing works

Martin: Mobility-Aware Reputation Mechanism for Federated Learning

Liu, L., Chang, X., Yang, H., Wang, J., Zhang, X., & Shi, C. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889089 · Cited by 2 · see citing works

Autonomous Federated Learning Architectures for Scalable IoT Networks: Enhancing Distributed Intelligence with Privacy Preservation

Arigela, A. K., Brahmareddy, A., Selvan, M. P., Sreenivas, T. S., Venu, N., & Ansari, A. A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8687-2_40 · Cited by 2 · see citing works

Federated Learning for Internet of Medical Things

Tanwar, S., Verma, A., & Bhattacharya, P. (2023) · CRC Press eBooks · doi:10.1201/9781003303374 · Cited by 1 · see citing works

A systematic review of the foundations, applications, and challenges of federated learning

Fateh, H., Rezvani, M., & Tahanian, E. (2024) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.21.3.23 · Cited by 1 · see citing works

Local Drift Correction and Attention Aggregation for Self-Organized Federated Learning

Liu, H., Gu, R., Wang, J., Zhang, X., Zhang, B., & Li, X. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00123 · Cited by 1 · see citing works

Towards A Common Task Framework for Distributed Collaborative Machine Learning

Liao, Q., Landuyt, D. V., Preuveneers, D., & Joosen, W. (2025) · IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw64639.2025.10962497 · Cited by 1 · see citing works

A Survey on Privacy-Preserving Federated Learning in Internet of Things (IoT)

Raghav, S., Krishna, B. V., & Vanipriya, C. (2025) · International Conference on Next Generation Communication & Information Processing (INCIP) · doi:10.1109/incip64058.2025.11019612 · Cited by 1 · see citing works

Federated AIoT Framework for Privacy-Preserving Smart Grid Management

Muhammad-Bello, B. L., Kusharki, M. B., Misra, S., & Galadima, U. N. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9137-2.ch005 · Cited by 1 · see citing works

Protecting Privacy in IoT-Based Deep Learning: State-of-the-Art Methods and Challenges

Nocker, M., Merkle, F., Schöttle, P., & Janetschek, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01799-4_10 · Cited by 1 · see citing works

A collaborated federated learning for healthcare informatics: solution and challenges

Joshi, R., Kumari, S., Pandey, K., & Badola, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33789-5.00024-8 · Cited by 1 · see citing works

Ethical and Privacy-Aware AI in SIoT

Virmani, A., Mishra, A. K., Dudhagara, C. R., Mishra, R. K., & Mishra, R. P. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-10122-8_18 · Cited by 1 · see citing works

A Privacy-Preserving Federated Learning with Mutual Verification on Vector Spaces

Zhang, M., Cui, C., Shen, G., & Zhang, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-7242-3_14 · Cited by 1 · see citing works

Privacy-Preserving Machine Learning Techniques for IoT Data in Cloud Environments

Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Chaudhary, S., & Gupta, M. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-0766-3.ch006 · Cited by 1 · see citing works

Application of differential privacy to sensor data in water quality monitoring task

Arzovs, A., Parshutin, S., Urbanovičs, V., Rubulis, J., & Dejus, S. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103019 · Cited by 1 · see citing works

A Systematic Survey of Architectural Approaches and Trade-Offs in Data De-identification

Landuyt, D. V., & Joosen, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16697-6_5 · Cited by 1 · see citing works

Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated LearningPreprint

Yang, W., Zhou, Y., Hu, M., Wu, D., Zheng, X., Wang, H., & Guo, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00404 · Cited by 1 · see citing works

Applications of Machine Learning Algorithms in Data Encryption Standards

Subashini, V., & Janaki, R. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-4159-9.ch008

Advanced Architectures and Innovative Platforms for Federated Learning: A Comprehensive Exploration

Bhati, N., & Vyas, N. (2024) · doi:10.1002/9781394219230.ch8

License Plate Recognition Using Federated Learning

Prajwal, B. S., Madagaonkar, S. B., & Makkithaya, K. (2024) · International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) · doi:10.1109/mpcit62449.2024.10892789

Using Machine Learning to Deal with Privacy and Confidentiality in Internet of Things: An Overview

Kandil, H., & Benaboud, H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-88653-9_73

An Investigation on the Backdoor Framework Model of Federated Learning for Improving Network Security

Hussain, G. K. J., & Manoj, G. (2025) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla63829.2025.11040496

A Study on Privacy-Focused Machine Learning in IoT Networks via Federated Learning

Jisna Jaison, T., & Thilakavathy, P. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330425

User Centric Escalation Policies for Adaptive Women Safety Orchestration

Divya, P., Kumaresan, M., & Manikandan, P. (2025) · International Conference on NexGen Networks and Cybernetics (IC2NC) · doi:10.1109/ic2nc67409.2025.11376405

A Privacy-Preserving Data Pipeline Architecture for Federated and Generative AI Collaboration

Venkatesan, V., Balakumar, G., Vasudevan, G., Balu, G. P., & Saha, P. (2026) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc67433.2026.11393736

Federated Learning and Differential Privacy Preserve for Precise Disease Prediction

Sundar, K., Ravikumar, S., Babu, R., Eugene Berna, I., & K, V. (2025) · International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM) · doi:10.1109/iipem65914.2025.11548043

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

Reinforcement and Federated Learning based Self Healing Architecture for Smart City

Chitra, K., Perumalsamy, J., Kotapati, V. B. R., Thilagaraj, S., Rambabu, V. P., & Soundarapandiyan, R. (2026) · International Conference Inventive Material Science Applications · doi:10.1109/icima68728.2026.11564130

A Comprehensive Review of Privacy-Preserving Techniques in Federated Learning for IoT Networks

Lawson, J., & Wilson, D. (2026) · Journal of Global IT Dynamics · doi:10.67808/jgidv1i1-001-260807110403

Gradient perturbation gaussian assisted privacy preserving mechanism for IIoT data transmission in federated learning

Kaushik, A. K., Kandhoul, N., Dhurandher, S. K., & Sharma, D. K. (2026) · Cluster Computing · doi:10.1007/s10586-026-06476-0

A Review of Privacy Preserving Federated Learning for Private IoT Analytics.Preprint

Briggs, C. C., Fan, Z., & András, P. (2020) · arXiv (Cornell University) · Cited by 14 · see citing works

Federated Learning for Internet of Things

Li, Y., Zhang, Q., Wang, X., Zeng, R., Li, H., Murturi, I., Dustdar, S., & Huang, M. (2023) · Springer eBooks · doi:10.1007/978-3-031-50514-0_3 · Cited by 5 · see citing works

The Synergy of Federated Learning and IoT: Pioneering Privacy and Efficiency in Decentralised Systems

Mukund, A., Varun, T. A., & Sundharakumar, K. B. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-98360-3_6

A Survey on Privacy-Preserving Federated Learning in Internet of Things (IoT)

Raghav, S., Krishna, B. V., & Vanipriya, C. (2025) · International Conference on Next Generation Communication & Information Processing (INCIP) · doi:10.1109/incip64058.2025.11019612 · Cited by 1 · see citing works

Federated Learning and Its Role in the Privacy Preservation of IoT Devices

Alam, T., & Gupta, R. (2022) · Future Internet · doi:10.3390/fi14090246 · Cited by 84 · see citing works