Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

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Federated learning for predicting compound mechanism of action based on image-data from cell painting

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Towards Communication-Efficient Federated Learning via Sparse and Aligned Adaptive Optimization

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Deep Learning Based Coded Over-the-Air Computation for Personalized Federated Learning

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POP-FL: Towards Efficient Federated Learning on Edge Using Parallel Over-Parameterization

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Multi-Attribute Auction-Based Grouped Federated Learning

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Automatic Intelligent Chronic Kidney Disease Detection in Healthcare 5.0

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A Vertical Federated Learning Method for Electric Vehicle Charging Station Load Prediction in Coupled Transportation and Power Distribution Systems

Han, Q., & Li, X. (2025) · Processes · doi:10.3390/pr13020468 · Cited by 5

Adaptive aggregation for federated learning using representation ability based on feature alignment

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Intelligent UAV Deployment for Energy-Efficient IoT Data Collection

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Federated Fine-Tuning Performance on Edge Devices

Orescanin, M., Ergezer, M., Singh, G., & Baxter, M. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00191 · Cited by 4

A Secure Federated Learning Mechanism for Data Privacy Protection

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Communication-Efficient Federated Indoor Localization with Layerwise Swapping Training-FedAvg

LIANG, J., Liu, Z., Zhou, Z., & Yan, X. U. (2022) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2022eap1004 · Cited by 4

Energy-efficient Federated Learning via Stabilization-aware On-device Update Scaling

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Structured Bayesian Federated Learning for Green AI: A Decentralized Model Compression Using Turbo-VBI-Based Approach

Xia, C., Tsang, D. H. K., & Lau, V. K. N. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3335971 · Cited by 4

Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning

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Policy-Based Federated LearningPreprint

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Hierarchical Federated Learning in Inland Waterways via Dataset Distillation and Resource Allocation

Zhao, J., Li, B., Yang, T., & Liu, J. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3497219 · Cited by 5

FedBDQB: Communication Efficient Federated Learning via Bidirectional Dynamic Quantization and Bitmap

Liu, B., Gao, Y., Zhang, C., & Zhong, G. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978684 · Cited by 3

FedCoRE: Effective Federated Learning for constrained RESTful environments in the Artificial Intelligence of Things

Djamaa, B., Yekhlef, H., Kouda, M. A., & Bradai, A. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104357 · Cited by 3

Lightweight Federated Learning in Mobile Edge Computing With Statistical and Device Heterogeneity Awareness

Tan, J., Zhang, Z., Guo, K., Chang, T., & Quek, T. Q. S. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3627679 · Cited by 5

A two-stage multi-branch fusion model for human activity recognition based on sensor data transformed by gramian angular difference field

Lu, L., & Deng, T. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109141 · Cited by 3

Mellin transform-based D2D power optimization in 5G-enabled social IoT network

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The Role of SDN to Improve Client Selection in Federated Learning

Mahmod, A., Pace, P., & Iera, A. (2024) · IEEE Communications Magazine · doi:10.1109/mcom.003.2400093 · Cited by 3

An Improved Small-World Topology for Optimizing the Performance of Echo State Network

Sha, G., Yu, P. H., Qi, D., HanLiang, X., ZhenKai, C., & TianLu, H. (2020) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00211 · Cited by 3

PhD Forum Abstract: Efficient Computing and Communication Paradigms for Federated Learning Data Streams

Bano, S. (2021) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp52413.2021.00086 · Cited by 3

Incentive Mechanism for AI-Based Mobile Applications with Coded Federated Learning

Saputra, Y. M., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685867 · Cited by 3

Privacy-Preserving Deep Learning in Internet of Healthcare Things with Blockchain-Based Incentive

Zhang, W., Li, P., Wu, G., & Li, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10989-8_24 · Cited by 3

ePMLF: Efficient and Privacy‐Preserving Machine Learning Framework Based on Fog Computing

Zhao, R., Xie, Y., Cheng, H., Jia, X., & Shirazi, S. H. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8292559 · Cited by 3

Towards Distributed Learning to Support Situational Awareness for Robotic Team Augmented Humanitarian Disaster Response

Allison, M., Farmer, M. E., & Song, Z. (2024) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc60891.2024.10427713 · Cited by 3

FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks

Shkurti, L., Selimi, M., & Besimi, A. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-54531-3_9 · Cited by 3

MG2FL: Multi-Granularity Grouping-Based Federated Learning in Green Edge Computing Systems

Dai, Z., Zhao, Y., Qiu, C., Wang, X., & Yu, F. R. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437153 · Cited by 3

Enhancing Privacy in Distributed Intelligent Vehicles With Information Bottleneck Theory

Xu, X., Liu, P., Zhao, Y., Han, L., Wang, W., Zhu, Y., Zhang, C., Wang, B., Shen, J., & Han, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3434627 · Cited by 3

Enhancing Global Model Performance in Federated Learning With Non-IID Data Using a Data-Free Generative Diffusion Model

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Joint Job Assignment and Resource Allocation for Multi-Job Wireless Federated Learning

Li, T. K., Wei, Z., Liu, H., Lin, Z., & Chan, T.-T. (2024) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass62177.2024.00062 · Cited by 3

CFLMEC: Cooperative Federated Learning for Mobile Edge Computing

Wang, X., Zhong, X., Yang, Y., Yang, T., & Cheng, N. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839263 · Cited by 3

Privacy-preserving data analytics

Zhao, Y. (2022) · doi:10.32657/10356/160032 · Cited by 3

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

Duan, Q., Huang, J. G., Hu, S., Deng, R., Lu, Z., & Yu, S. (2022) · doi:10.36227/techrxiv.21788450.v1 · Cited by 4

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

Privacy-preserving transfer learning framework for building energy forecasting with fully anonymized data

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Over-the-Air Computing Aided Federated Learning and Analytics via Belief Propagation Based Stochastic Bandits

Chen, Z., Tong, J., Fu, L., & Han, Z. (2022) · ICC 2022 - IEEE International Conference on Communications · doi:10.1109/icc45855.2022.9839106 · Cited by 3

Federated Representation Learning for Indoor-Outdoor Detection in Beyond 5G Networks

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Adaptive Multi-Criteria Selection for Efficient Resource Allocation in Frugal Heterogeneous Hadoop Clusters

Qureshi, B. (2024) · Electronics · doi:10.3390/electronics13101836 · Cited by 2

A Practical Evaluation of a Federated Learning Application in IoT Device and Cloud Environment

Barbosa, G. N. N., & Mattos, D. M. F. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.4324 · Cited by 2

Prevention of Data Privacy and Recommendations using Federated Graph Neural Network

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An Asynchronous Federated Learning Optimization Scheme Based on Model Partition

Xu, J., Shi, L., Shi, Y., Chen, F., & Xu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19211-1_31 · Cited by 2

The Role of Memory in Social Learning When Sharing Partial Opinions

Cirillo, M., Bordignon, V., Matta, V., & Sayed, A. H. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096186 · Cited by 2

Decentralized AI for Edge Devices with Federated Learning in the Internet of Things

S, G., Nagarajan, M., Hussain, M. W., Jayachitra, T., Jothikumar, R., & Sathya, S. (2023) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses60034.2023.10465329 · Cited by 2

SURVEY ON TECHNICAL ADVANCEMENTS AND RENOVATIONS IN FEDERATED LEARNING

Subramanian, B., Tulshain, S., MananModi, Dalei, A., & Sumathi, D. (2024) · Malaysian Journal of Computer Science · doi:10.22452/mjcs.vol37no1.2 · Cited by 2

On ECG Signal Classification: An NAS-empowered Semantic Communication System

Xing, H., Ma, H., Xiao, Z., Wang, X., Zhao, B., Luo, S., Feng, L., & Xu, L. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00078 · Cited by 2

A Time‐Overlapping Multiplex VLC System for End‐Edge Data Transmission

Fu, T., Zhu, H., Hai, H., & Lao, H. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/9970972 · Cited by 2

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

FedIoT: Optimizing the Communication-Efficient Federated Learning Aggregation Algorithm Under Heterogeneous Data for Large-Scale IoT

Yan, Q., Wang, G., Zhu, D., & Li, J. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425520135 · Cited by 1

Deep Reinforcement Learning for Delay Minimization in MEC-THz Networks with Finite Blocklength codes

Ahmadi, A., Høst-Madsen, A., & Xiong, Z. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11101710 · Cited by 1

Communication Efficient Over-the-Air Federated Learning With Random FLARE Algorithm

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FOVA: Offline Federated Reinforcement Learning With Mixed-Quality Data

Qiao, N., Yue, S., Ren, J., & Zhang, Y. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3637043 · Cited by 1

QIPPO/CA: A Quantized Communication-Efficient MARL Framework for Fully Distributed Channel Access in Next-Generation Wireless Networks

Hong, S., Jeong, Y., Hwang, U., & Hong, S. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3640686 · Cited by 1

SLiM-V2I: Selective Layer Merging for Communication-Efficient FL in V2I Networks

Mertens, J. S., Jeniffer, J. T., Galluccio, L., & Morabito, G. (2025) · International Conference on Federated Learning Technologies and Applications (FLTA) · doi:10.1109/flta67013.2025.11336334 · Cited by 1

CSFL: Communication-Efficient Semi-Asynchronous Federated Learning Method in Resource-Constrained Edge Computing

Deng, J., Liu, J., Liu, Y., Hu, C., Li, Y., Tao, Y., Yang, Y., Wang, H., & Yan, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3706440 · Cited by 1

A cross-blockchain based distributed crowdsouring system for Internet of Things

Peng, M., Miao, Q., Lin, H., & Wang, X. (2020) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00062 · Cited by 1

A Reinforcement Learning-based Task Classification Mechanism for Privacy-Enhanced Mobile Crowdsensing Strategy

Peng, M., Lin, H., & Wang, X. (2021) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc-cit-dsci-smartcns55181.2021.00029 · Cited by 1

In-Network Caching and Learning Optimization for Federated Learning in Mobile Edge Networks

Saputra, Y. M., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839229 · Cited by 1

Performance Analysis of Federated Learning in wireless networks

Joomye, A., Tahir, M., Sheikh, M. A., Ling, M. H., & Meng, Y. K. (2022) · IEEE International Symposium on Telecommunication Technologies · doi:10.1109/istt56288.2022.9966534 · Cited by 1

FedRPO: Federated Relaxed Pareto Optimization for Acoustic Event Classification

Feng, M., Kao, C.-C., Tang, Q., Solomon, A., Rozgić, V., & Wang, C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097217 · Cited by 1

Federated Learning with Client Selection in Resource-Uncertain Wireless Networks: Simulation and Proof of Concept Experiments

Yoshida, N., Nishio, T., Yamamoto, K., & Morikura, M. (2023) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-29301-6_11 · Cited by 1

Parameter-less Asynchronous Federated Learning under Computation and Communication Constraints

Wu, M., Boban, M., & Dressler, F. (2023) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2023-spring57618.2023.10200520 · Cited by 1

Efficient AIoT Resource Management Techniques Reflecting Virtualization Resource Allocation in Production Environments

Jeong, Y.-S., & Kim, Y.-T. (2023) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2023.21.8.121 · Cited by 1

A Comparison of Federated Aggregation Strategies and Architectures for Next-word Prediction

Sakhnovych, Y., Röttger, R., & Mayer, R. L. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386579 · Cited by 1

Stackelberg Game-Base Incentive Scheme for Federated Learning in Artificial Intelligence of Things

Qin, S., Ye, J., Tang, X., & Li, X. (2023) · 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-cybermatics60724.2023.00049 · Cited by 1

BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis

Wani, S., Zhou, X., & Shimizu, S. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00114 · Cited by 1

Modeling, Simulating, and Evaluating Complex End-to-End Edge Intelligence Systems

Kuttivelil, H., & Obraczka, K. (2024) · Internet of things · doi:10.1007/978-3-031-58388-9_1 · Cited by 1

Federated Learning Models for Real-Time IoT: A Survey

Houidi, K., Said, M. S., Hakiri, A., Mellouli, N., & Ayed, H. K. B. (2024) · IEEE International Symposium on Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc61049.2024.10551367 · Cited by 1

Data‐sharing strategies in medical consortium based on master‐slave multichain and federated learning

Kang, B., Zhang, N., & Zhu, J. (2024) · IET Blockchain · doi:10.1049/blc2.12075 · Cited by 1

A Federated Learning Approach For Operator Monitoring in Heterogeneous Cobot Environments

Kianoush, S., Minora, A., Savazzi, S., & Dai, M. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10711164 · Cited by 1

An optimal scheme for speeding the training process of the asynchronous federated learning based on model partition

Shi, L., Ren, Y., Xu, J., Xie, Y., Chen, F., & Fan, Y. (2025) · Computing · doi:10.1007/s00607-025-01418-x · Cited by 1

AIoT processing techniques for efficiently extracting and analyzing large amounts of IoT information

Jeong, Y. S. (2021) · IJEMR · doi:10.22662/ijemr.2021.5.1.001 · Cited by 1

Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge ClustersPreprint

Qureshi, B. (2024) · Preprints.org · doi:10.20944/preprints202404.0154.v2 · Cited by 1

Optimization Of Heterogeneous Iot Information Using Blockchain-Based Multiple Polynomials To Minimize Data Delay In Cloud Edge Environments

Shin, S.-S. (2021) · Turkish Journal of Computer and Mathematics Education (TURCOMAT) · doi:10.17762/turcomat.v12i9.4775

Resource Allocation in Multi-access Edge Computing (MEC) Systems: Optimization and Machine Learning Algorithms

Zarandi, S. (2021) · YorkSpace (York University)

Smart Detection and Preservation of Privacy Concerns in Iot Systems: A Systematic Literature ReviewPreprint

Giordano, G., Palomba, F., & Ferrucci, F. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3979390

Efficiency-optimized Data Perturbation in Local Differentially Private Federated LearningPreprint

Zhao, J., Yang, M., Zheng, J., Feng, J., & Matwin, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1890894/v1

A Communication-Efficient Federated Learning: A Probabilistic Approach

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Proceedings of the 1st International Workshop on Networked AI Systems

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Análise Prática da Técnica de Aprendizado Federado Aplicada a Dispositivos da Internet das Coisas

Barbosa, G. N. N., & Mattos, D. M. F. (2023) · doi:10.5753/wperformance.2023.231129

A Traffic Flow Prediction Method Based on Blockchain and Federated LearningPreprint

Zhi, H., 段, 苗., & Yang, L. (2023) · Research Square · doi:10.21203/rs.3.rs-3419449/v1

GreenEdge: Neural-enhanced Green Workload Coordination for Ubiquitous Edge Intelligence

Xu, T. Z., & Ng, A. K. Y. (2023) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2023-fall60731.2023.10333403

Efficient Federated Learning for Feature Aggregation with Heterogenous Edge Devices

Liu, F., Xiong, Z., Yu, W., Wu, J. X., Kong, N., Ji, Y., Xu, S., & Ji, M. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2665/1/012007

Autonomous Vehicle Systems in Intelligent Interconnected Transportation Networks

Chronis, C., Tserpes, K., & Varlamis, I. (2024) · Wiley eBooks · doi:10.1002/9781394188789.ch15

FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting

Seo, J., Rong, C., & Kim, M. (2025) · IEEE Access · doi:10.1109/access.2025.3648064

Fed-DSP: Federated Learning with Dynamic Sparse Perturbation

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Big Data Assisted Object Detection with Privacy Protection

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