Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

Untitled work

(2010) · Cited by 8,261

Federated Learning: Strategies for Improving Communication Efficiency

Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232

Federated Learning with Non-IID Data

Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077

Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725

Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566

Federated Learning for Mobile Keyboard Prediction

Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Lin, Y., Han, S., Mao, H., Wang, Y., & Dally, W. J. (2017) · arXiv · doi:10.48550/arxiv.1712.01887 · Cited by 1,134

Learn to Collaborate in MEC: An Adaptive Decentralized Federated Learning Framework

Wang, Y., Wen, Z., Li, Y., & Cao, B. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3439588 · Cited by 13

Enhancing Federated Learning Convergence With Dynamic Data Queue and Data-Entropy-Driven Participant Selection

Herath, C., Liu, X., Lambotharan, S., & Rahulamathavan, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3491034 · Cited by 13

A Review on Deep Reinforcement Learning for the management of SDN and NFV in Edge-IoT

Alonso, R. S., Prieto, J., la Prieta, F. D., Rodríguez, S., & Corchado, J. M. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682179 · Cited by 15

Federated Learning via Attentive Margin of Semantic Feature Representations

Michieli, U., Toldo, M., & Özay, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3209865 · Cited by 13

An Empirical Study of Federated Learning on IoT–Edge Devices: Resource Allocation and Heterogeneity

Wong, K., Nguyen-Duc, M., Le, K. H., Ho, L., Do, C., & Le-Phuoc, D. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3611415 · Cited by 27

KafkaFed: Two-Tier Federated Learning Communication Architecture for Internet of Vehicles

Bano, S., Tonellotto, N., Cassará, P., & Gotta, A. (2022) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops53856.2022.9767510 · Cited by 12

IoV environment exploring coordination: A federated learning approach

Ding, T. C., Liu, L., Zhu, Y., Cui, L., & Yan, Z. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.07.006 · Cited by 12

An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection

Kholidy, H. A., & Kamaludeen, R. (2022) · IEEE Future Networks World Forum (FNWF) · doi:10.1109/fnwf55208.2022.00033 · Cited by 14

AutoFL: Towards AutoML in a Federated Learning Context

Preuveneers, D. (2023) · Applied Sciences · doi:10.3390/app13148019 · Cited by 12

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT

He, P., Lan, C., Bashir, A. K., Wu, D., Wang, R., Kharel, R., & Yu, K. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3298446 · Cited by 12

FAC-fed: Federated adaptation for fairness and concept drift aware stream classification

Badar, M., Nejdl, W., & Fisichella, M. (2023) · Machine Learning · doi:10.1007/s10994-023-06360-7 · Cited by 12

FLOW: A Robust Federated Learning Framework to Defend Against Model Poisoning Attacks in IoT

Liu, S., Li, Z., Sun, Q., Chen, L., Zhang, X., & Li, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3341811 · Cited by 13

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

Naseh, D., Abdollahpour, M., & Tarchi, D. (2024) · Information · doi:10.3390/info15040190 · Cited by 12

Flexible Training and Uploading Strategy for Asynchronous Federated Learning in Dynamic Environments

Wu, M., Boban, M., & Dressler, F. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3418613 · Cited by 12

AEFL: Anonymous and Efficient Federated Learning in Vehicle–Road Cooperation Systems With Augmented Intelligence of Things

Wang, X., Li, J., Lin, H., Dai, C., Garg, S., & Kaddoum, G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3446725 · Cited by 12

Fair swarm learning: Improving incentives for collaboration by a fair reward mechanism

Tajabadi, M., & Heider, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112451 · Cited by 11

Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT

Ding, Q., Yue, X., Zhang, Q., Xiong, Z., Chang, J., & Zheng, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3485208 · Cited by 12

A Contribution-Aware Federated Framework for Electric Vehicle Batteries Health Estimation

Chen, B., Fan, L., Zeng, X., Gu, M., & Zhou, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3524005 · Cited by 14

Cyber-Physical AI: Systematic Research Domain for Integrating AI and Cyber-Physical Systems

Lee, S., Chae, J., Jeon, H., Kim, T., Hong, Y.-G., Um, D., Kim, T., & Park, K. (2025) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3721437 · Cited by 20

Asymptotic Analysis of Federated Learning Under Event-Triggered Communication

He, X., Yi, X., Zhao, Y., Johansson, K. H., & Gupta, V. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3295734 · Cited by 11

Wind power forecasting based on ensemble deep learning with surrogate-assisted evolutionary neural architecture search and many-objective federated learning

Jin, H., Zhang, K., Fan, S., Jin, H., & Wang, B. (2024) · Energy · doi:10.1016/j.energy.2024.133023 · Cited by 11

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

Liu, J., Jia, J., Zhang, H., Yun, Y., Wang, L., Zhou, Y., Dai, H., & Dou, D. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3690648 · Cited by 12

A Framework for Smart City Traffic Management utilizing BDA and IoT

Nagalapuram, J., & Samundeeswari, S. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8003 · Cited by 12

Weighted Average Consensus Algorithms in Distributed and Federated Learning

Tedeschini, B. C., Savazzi, S., & Nicoli, M. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3528982 · Cited by 8

Spread: Decentralized Model Aggregation for Scalable Federated Learning

Hu, C., Liang, H. H., Han, X. M., Liu, B. A., Cheng, D., & Wang, D. (2022) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3545008.3545030 · Cited by 11

IoT systems with multi-tier, distributed intelligence: From architecture to prototype

GabAllah, N. A., Farrag, I., Khalil, R., Sharara, H., & ElBatt, T. (2023) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101818 · Cited by 12

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence.Preprint

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2021) · arXiv (Cornell University) · Cited by 11

A Method of Self-Supervised Denoising and Classification for Sensor-Based Human Activity Recognition

Lu, L., & Deng, T. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3323314 · Cited by 10

A Ring Topology-Based Communication-Efficient Scheme for D2D Wireless Federated Learning

Xu, Z., Tian, W., Liu, Y., Ning, W., & Wu, J. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437407 · Cited by 10

Mutual Knowledge Distillation-Based Personalized Federated Learning for Smart Edge Computing

Zheng, S., Hu, J. W., Min, G., & Li, K. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3412817 · Cited by 12

Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains

Schoinas, Ι., Triantafyllou, A., Ioannidis, D., Tzovaras, D. K., Drosou, A., Votis, K., Λάγκας, Θ., Argyriou, V., & Sarigiannidis, P. G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3458088 · Cited by 10

Federated Reinforcement Learning for Intelligent Route Planning in Aerial-Terrestrial Network

Ding, T. C., Liu, L., Yan, Z., Lu, X., & Hu, J. W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3492215 · Cited by 10

Where is the Testbed for My Federated Learning Research?

Božič, J., Faustino, A. R., Radovič, B., Canini, M., & Pejović, V. (2024) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec62691.2024.00027 · Cited by 13

Collaborative intrusion detection in resource-constrained IoT environments: Challenges, methods, and future directions a review

Ieropoulos, V., Anthi, E., Spyridopoulos, T., Burnap, P., Mavromatis, I., Khan, A., & Carnelli, P. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104127 · Cited by 11

Engineering Federated Learning Systems: A Literature Review

Zhang, H., Bosch, J., & Olsson, H. H. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-67292-8_17 · Cited by 10

Federated Adam-Type Algorithm for Distributed Optimization With Lazy Strategy

Li, Y., Li, W., Zhang, B., & Du, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3175997 · Cited by 10

Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning

Becking, D., Kirchhoffer, H., Tech, G., Haase, P., Müller, K., Schwarz, H., & Samek, W. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00380 · Cited by 10

Unlinkable Collaborative Learning Transactions: Privacy-Awareness in Decentralized Approaches

Rahmadika, S., & Rhee, K.-H. (2021) · IEEE Access · doi:10.1109/access.2021.3076205 · Cited by 10

Analysis and evaluation of synchronous and asynchronous FLchain

Wilhelmi, F., Giupponi, L., & Dini, P. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109390 · Cited by 10

Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

Wang, H., Shen, H., Li, F., Wu, Y., Li, M., Shi, Z., & Deng, F. (2023) · Electronics · doi:10.3390/electronics12030730 · Cited by 10

Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy

Wang, F., Gursoy, M. C., & Velipasalar, S. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3408131 · Cited by 10

Adaptive Federated Deep Learning-Based Semantic Communication in the Social Internet of Things

Tan, W., Wang, Y., Liu, L., Wang, X., & Ding, T. C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3484230 · Cited by 10

Federated Learning for 6G Security: A Survey on Threats, Solutions, and Research Directions

de Alwis, C., Aouedi, O., Xu, J., Wang, S., Siriwardhana, Y., Hewa, T. M., Zeydan, E., Sandeepa, C., & Liyanage, M. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3663434 · Cited by 16

Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks

Tao, Y., Jiang, Y., Zheng, F., Wang, Z., Zhu, P., Tao, M., Niyato, D. T., & You, X. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3229679 · Cited by 10

Energy-Efficient Federated Edge Learning in Multi-Tier NOMA-Enabled HetNet

Hossain, M. A., & Ansari, N. (2023) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2023.3285534 · Cited by 9

Communication efficient federated learning with data offloading in fog-based IoT environment

Kumari, N., & Jana, P. K. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.051 · Cited by 9

Split Consensus Federated Learning: An Approach for Distributed Training and Inference

Tedeschini, B. C., Brambilla, M., & Nicoli, M. (2024) · IEEE Access · doi:10.1109/access.2024.3446577 · Cited by 9

LightFIDS: Lightweight and Hierarchical Federated IDS for Massive IoT in 6G Network

Alotaibi, A. I., & Barnawi, A. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08439-8 · Cited by 9

Collaborative deployment of Large AI Models on the edge: A microservice approach to heterogeneous training and quantized inference

Ahmadpanah, S. H., Mirabi, M., Sobhanloo, S., Afsharfarnia, P., Fallah, D., & Bayati, M. (2025) · Ad Hoc Networks · doi:10.1016/j.adhoc.2025.104080 · Cited by 8

Federated Routing Scheme for Large-scale Cross Domain Network

Zhang, Y., Tian, Y., Wang, W., Cong, P., Chen, C., Li, D., & Xu, K. (2020) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps50562.2020.9162750 · Cited by 8

Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning

Seo, S., Lee, J., Ko, H., & Pack, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3262582 · Cited by 8

A Novel Uplink Coverage and Capacity Enhancement Scheme in NR TDD Network

Guo, B., Guo, J., Zhang, L., Fang, Y., Meng, Y., & Li, J. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00321 · Cited by 8

Latency and Energy Minimization in NOMA-Assisted MEC Network: A Federated Deep Reinforcement Learning Approach

Ahmadi, A., Høst-Madsen, A., & Xiong, Z. (2024) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc61673.2024.10733563 · Cited by 8

Communication-Efficient Semihierarchical Federated Analytics in IoT Networks

Zhao, L., Valero, M., Pouriyeh, S., Li, L., & Sheng, Q. Z. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3137757 · Cited by 8

An Energy-efficient and Privacy-aware Decomposition Framework for Edge-assisted Federated Learning

Shi, Y., Duan, H., Yang, L., & Cai, W. (2022) · ACM Transactions on Sensor Networks · doi:10.1145/3522741 · Cited by 8

Performance-Aware Client and Quantization Level Selection Algorithm for Fast Federated Learning

Seo, S., Lee, J., Ko, H., & Pack, S. (2022) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc51071.2022.9771600 · Cited by 8

Privacy-Enhanced Federated Learning: A Restrictively Self-Sampled and Data-Perturbed Local Differential Privacy Method

Zhao, J., Yang, M., Zhang, R., Song, W., Zheng, J., Feng, J., & Matwin, S. (2022) · Electronics · doi:10.3390/electronics11234007 · Cited by 10

An Efficient Distributed Machine Learning Framework in Wireless D2D Networks: Convergence Analysis and System Implementation

Cheng, K., Guo, F., & Peng, M. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3234550 · Cited by 8

Spectrum Challenges in the Internet of Things: State of the Art and Next Steps

Restuccia, F., Melodia, T., & Ashdown, J. (2022) · Wiley eBooks · doi:10.1002/9781119892199.ch19 · Cited by 8

Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning

Forootani, A., & Iervolino, R. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3648504 · Cited by 9

Efficient Transmission and Secure Sharing of Sensing data under Distributed ISAC Conditions

Mu, J., Jing, Z., Cui, Y., Jing, X., Zhou, Q., & Ouyang, W. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10183227 · Cited by 7

A Three-Level Federated Learning Framework for CSI Fingerprint Based Indoor Localization in Multiple Servers Environment

Yan, J., Cui, Y., & Wang, W. (2024) · IEEE Communications Letters · doi:10.1109/lcomm.2024.3357694 · Cited by 7

Federated Learning Using Multi-Modal Sensors with Heterogeneous Privacy Sensitivity Levels

Hsu, C.-F., Li, Y.-C., Tsai, C.-C., Wang, J.-K., & Hsu, C.-H. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3686801 · Cited by 7

Federated deep learning models for detecting RPL attacks on large-scale hybrid IoT networks

Albishari, M., Li, M., Ayoubi, M., Alsanabani, A. A., & Tian, J. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110837 · Cited by 8

Dynamic Resource Optimization for Adaptive Federated Learning Empowered by Reconfigurable Intelligent Surfaces

Battiloro, C., Merluzzi, M., Lorenzo, P. D., & Barbarossa, S. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746891 · Cited by 7

Sparse Communication for Federated Learning

Thonglek, K., Takahashi, K., Ichikawa, K., Nakasan, C., Leelaprute, P., & Iida, H. (2022) · IEEE International Conference on Fog and Edge Computing (ICFEC) · doi:10.1109/icfec54809.2022.00008 · Cited by 7

DLDP-FL: Dynamic local differential privacy federated learning method based on mesh network edge devices

Yin, K., Wu, B. Y., Zhu, R., Xiao, L. W., Tan, Z., He, G., Wang, Z., & Yin, G. (2022) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101789 · Cited by 7

Communication-Efficient Federated Learning in Channel Constrained Internet of Things

Hu, T., Zhang, X., Chang, Z., Hu, F., & Hämäläinen, T. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000898 · Cited by 7

Attribute recognition for person re-identification using federated learning at all-in-edge

Girija, S., Baker, T., Ahmed, N., Khedr, A. M., Aghbari, Z. A., Jha, A., Соболев, К., Ahmadi‐Asl, S., & Phan, A. H. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100793 · Cited by 9

Improving the quality of Federated Learning processes via Software Defined Networking

Mahmod, A., Caliciuri, G., Pace, P., & Iera, A. (2023) · doi:10.1145/3597062.3597281 · Cited by 7

Toward Evaluating the Reliability of Deep-Neural-Network-Based IoT Devices

Fan, M., Liu, Y., Chen, C., Yu, S., Guo, W., Wang, L., & Liu, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3138969 · Cited by 8

Communication-Efficient Federated Deep Reinforcement Learning Based Cooperative Edge Caching in Fog Radio Access Networks

Zhang, M., Jiang, Y., Zheng, F., Wang, D., Bennis, M., Jamalipour, A., & You, X. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3467285 · Cited by 6

GeoFed: Geometry-Aware Byzantine Robust Federated Learning on SPD Manifolds in Heterogeneous Environments

Li, Q., Wu, Z., Xiong, J., Song, A., & Zhang, T. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3584818 · Cited by 6

Personalized Federated Learning in One-Shot: A Method for Heterogeneous Data Scenarios

Sang, T., Chu, Z., Jiang, X., Zhang, X., & Li, X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3589414 · Cited by 7

Performance Evaluation of Federated Learning in Edge Computing Environment

Kotecha, P., Dhoka, T., Bhatia, J., Kumhar, M., Gupta, R., Tanwar, S., & Jadav, N. K. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.279 · Cited by 8

Privacy Safeguarding for Human-Computer Interaction Based on Adaptive Federated Learning With Actor–Critic Selection

Jiang, B., Liu, Z., Yue, G., Cui, X., Liu, Y., & Wang, H. H. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3525186 · Cited by 6

Tinkertoy: Build Your Own Operating Systems for IoT Devices

Wang, B., & Seltzer, M. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3198907 · Cited by 6

HPFL: Federated Learning by Fusing Multiple Sensor Modalities with Heterogeneous Privacy Sensitivity Levels

Chen, Y., Hsu, C.-F., Tsai, C.-C., & Hsu, C.-H. (2022) · doi:10.1145/3552487.3556438 · Cited by 6

Communication-efficient Federated Learning Design with Fronthaul Awareness in NG-RANs

Younis, A., Sun, C., & Pompili, D. (2022) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass56207.2022.00089 · Cited by 6

FedFog - A federated learning based resource management framework in fog computing for zero touch networks

Khan, U. Y., Soomro, T. R., & Kougen, Z. (2023) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/muet1982.2303.08 · Cited by 6

A Novel Influence Maximization-based Dynamic Forwarding Node Selection Scheme in SDVNs

Zhao, L., Zheng, T., Lin, M., Hawbani, A., & Shang, J. (2020) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00110 · Cited by 6

Federated Action Recognition on Heterogeneous Embedded DevicesPreprint

Jain, P., Goenka, S., Bagchi, S., Banerjee, B., & Chaterji, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.12147 · Cited by 8

Effective Communication Compression Framework Using Federated Learning Privacy Model for Smart Grid

Shanmugam, D., & Arumugam, C. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.02.075 · Cited by 5

Optimizing Soil-Based Crop Recommendations with Federated Learning on Raspberry Pi Edge Computing Nodes

Mwawado, R. H., Zennaro, M., Nsenga, J., & Hanyurwimfura, D. (2024) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms62296.2024.10710260 · Cited by 5

Differentially Private and Communication-Efficient Federated Learning for AIoT: The Perspective of Denoising and Sparsification

Li, L., Liu, Z. C., Sun, X., Chang, L., Lan, R., Li, J., & Wang, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3520827 · Cited by 5

TIMPANY—deTectIon of Model Poisoning Attacks usiNg accuracY

Sameen, M., & Hwang, S. O. (2021) · IEEE Access · doi:10.1109/access.2021.3118926 · Cited by 5

Selective Federated Learning for Mobile Edge Intelligence

Yuan, X., Zhang, K., & Zhang, Y. (2021) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp52459.2021.9613518 · Cited by 5

Cloudlet Federation Based Context-Aware Federated Learning Approach

Latif, S., Nayyer, M. Z., Raza, I., Hussain, S. A., Jamal, M. H., Hur, S., & Ashraf, I. (2022) · IEEE Access · doi:10.1109/access.2022.3212550 · Cited by 5

Traffic Classification Method Based on Federated Semi-Supervised Learning

Sun, C., Chen, B., Bu, Y., & Zhang, D. (2022) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3573428.3573586 · Cited by 5

Memory-Aware Social Learning Under Partial Information Sharing

Cirillo, M., Bordignon, V., Matta, V., & Sayed, A. H. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3294615 · Cited by 5

Design of Advanced Privacy Preserving Model for Protecting Privacy within a Fog Computing Scenario

Jain, P., Thada, V., & Lala, A. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434728 · Cited by 5

Improving Privacy in Federated Learning-Based Intrusion Detection for IoT Networks

Syne, L., Caballero‐Gil, P., & Hernández‐Goya, C. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636183 · Cited by 5

SDN-Assisted Client Selection to Enhance the Quality of Federated Learning Processes

Mahmod, A., Pace, P., & Iera, A. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570525 · Cited by 5

Enhancing Sustainable Data Collection With Unmanned Aerial Vehicles: A Deep Reinforcement Learning Approach

Ding, T. C., Liu, L., Yan, Z., & Cui, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3461853 · Cited by 5

Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives

Muhammad, K., Ser, J. D., Magaia, N., Fonseca, R. W. S., Hussain, T., Gandomi, A. H., Daneshmand, M., & de Albuquerque, V. H. C. (2022) · IEEE Network · doi:10.1109/mnet.125.2100771 · Cited by 5

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Batool, H., Xu, J., Rehman, A. U., & Hamam, H. (2024) · Journal on artificial intelligence · doi:10.32604/jai.2024.049912 · Cited by 5

An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems.Preprint

Yu, X., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · arXiv (Cornell University) · Cited by 5

Security Provisions in Smart Edge Computing Devices using Blockchain and Machine Learning Algorithms: A Novel ApproachPreprint

Mishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Research Square · doi:10.21203/rs.3.rs-1714576/v1 · Cited by 5

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 · Cited by 5

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

The GAO-based federated learning framework with adaptive client selection for resource-efficient edge-IoT systems

Eslami, E., Fazeli, S. A. S., Abouei, J., Abbasi, E., & Karbassi, S. M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06449-3 · Cited by 2

Computation and Communication Efficient Adaptive Federated Optimization of Federated Learning for Internet of Things

Chen, Z., Cui, H., Wu, E., & Yu, X. (2023) · Electronics · doi:10.3390/electronics12163451 · Cited by 14

Communication-Efficient Federated Learning with Adaptive Consensus ADMM

He, S., Zheng, J., Feng, M., & Chen, Y. (2023) · Applied Sciences · doi:10.3390/app13095270 · Cited by 9

Communication Efficient Mobile Data Collection for IoT Edge using Federated Learning

Scholar, A. P. R. (2025) · Cuestiones de Fisioterapia · doi:10.48047/tz5fx475

Communication-Efficient Federated Learning in Channel Constrained Internet of Things

Hu, T., Zhang, X., Chang, Z., Hu, F., & Hämäläinen, T. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000898 · Cited by 7