Accelerating Federated Learning for IoT in Big Data Analytics With Pruning, Quantization and Selective Updating

Communication-Efficient Learning of Deep Networks from Decentralized Data

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Efficient Processing of Deep Neural Networks: A Tutorial and Survey

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Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

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Federated Learning: Strategies for Improving Communication Efficiency

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Pruning Filters for Efficient ConvNets

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Learning Efficient Convolutional Networks through Network Slimming

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Federated Learning in Mobile Edge Networks: A Comprehensive Survey

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BinaryConnect: Training Deep Neural Networks with binary weights during propagations

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Ternary Weight Networks

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Model Pruning Enables Efficient Federated Learning on Edge Devices

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Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues

Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399

Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions

Duan, Q., Huang, J., Hu, S., Deng, R., Lu, Z., & Yu, S. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3316615 · Cited by 189

Combining Edge Computing-Assisted Internet of Things Security with Artificial Intelligence: Applications, Challenges, and Opportunities

Rupanetti, D., & Kaabouch, N. (2024) · Applied Sciences · doi:10.3390/app14167104 · Cited by 102

Big Data Resource Management & Networks: Taxonomy, Survey, and Future Directions

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A survey on federated learning in data mining

Yu, B., Mao, W., Lv, Y., Zhang, C. B. C., & Xie, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1443 · Cited by 78

Green Federated Learning: A New Era of Green Aware AI

Thakur, D., Guzzo, A., Fortino, G., & Piccialli, F. (2025) · ACM Computing Surveys · doi:10.1145/3718363 · Cited by 77

Joint resource management for mobility supported federated learning in Internet of Vehicles

Wang, G., Xu, F., Zhang, H., & Zhao, C. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.020 · Cited by 50

Federated learning: A cutting-edge survey of the latest advancements and applications

Akhtarshenas, A., Vahedifar, M. A., Ayoobi, N., Maham, B., Alizadeh, T., Ebrahimi, S., & López-Pérez, D. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.107964 · Cited by 53

Complement Sparsification: Low-Overhead Model Pruning for Federated Learning

Jiang, X., & Borcea, C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25977 · Cited by 48

Federated learning for big data: A survey on opportunities, applications, and future directions

Gadekallu, T. R., Pham, Q., Huynh‐The, T., Feng, H., Fang, K., Pandya, S., Liyanage, M., Wang, W. W., & Nguyen, T. T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113614 · Cited by 86

BC-EdgeFL: A Defensive Transmission Model Based on Blockchain-Assisted Reinforced Federated Learning in IIoT Environment

Zhang, P., Hong, Y., Kumar, N., Alazab, M., Alshehri, M. D., & Jiang, C. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3116037 · Cited by 46

A Reference Architecture for Cloud–Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted Applications: Architectural Overview and Key Concepts

Trakadas, P. T., Masip‐Bruin, X., Facca, F. M., Spantideas, S., Giannopoulos, A., Kapsalis, N. C., Martins, R., Bosani, E., Ramon, J., Prats, R. G., et al. (2022) · Sensors · doi:10.3390/s22229003 · Cited by 41

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

Karras, A., Γιάνναρος, Α., Theodorakopoulos, L., Krimpas, G. A., Kalogeratos, G., Karras, C., & Sioutas, S. (2023) · Electronics · doi:10.3390/electronics12224633 · Cited by 43

A Survey on Collaborative Learning for Intelligent Autonomous Systems

dos Anjos, J. C. S., Matteussi, K. J., Orlandi, F. C., Barbosa, J. L. V., Silva, J. S., Bittencourt, L. F., & Geyer, C. F. R. (2023) · ACM Computing Surveys · doi:10.1145/3625544 · Cited by 38

Bearing Remaining Useful Life Prediction Using Federated Learning With Taylor-Expansion Network Pruning

Chen, X., Wang, H., Lu, S., & Yan, R. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3264027 · Cited by 33

Deep Compression for Efficient and Accelerated Over-the-Air Federated Learning

Khan, F. M. A., Abou-Zeid, H., & Hassan, S. A. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3373460 · Cited by 33

Distributed Pruning Towards Tiny Neural Networks in Federated Learning

Huang, H., Zhang, L., Sun, C., Fang, R., Yuan, X., & Wu, D. O. (2023) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00036 · Cited by 32

Optimized Convolutional Neural Network at the IoT edge for image detection using pruning and quantization

Naveen, S., & Kounte, M. R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20523-1 · Cited by 33

From Routine to Reflection: Pruning Neural Networks in Communication-Efficient Federated Learning

Pei, J., Li, W., & Mumtaz, S. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3462300 · Cited by 24

FedMef: Towards Memory-Efficient Federated Dynamic Pruning

Huang, H., Zhuang, W., Chen, C., & Lyu, L. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02601 · Cited by 24

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

FLOAT: Federated Learning Optimizations with Automated Tuning

Khan, A. F., Khan, A. A., Abdelmoniem, A. M., Fountain, S., Butt, A. R., & Anwar, A. (2024) · European Conference on Computer Systems · doi:10.1145/3627703.3650081 · Cited by 25

Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective

Alharbi, A., Alosaimi, W. A., Alyami, H., Alouffi, B., Almulihi, A. H., Nadeem, M., Sayeed, M. A., & Khan, R. A. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02651-8 · Cited by 21

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

Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment

Jia, Y., Liu, B., Zhang, X., Dai, F., Khan, A. A., Qi, L., & Dou, W. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3687478 · Cited by 22

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 20

Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Wang, A., Zhao, Y., Li, G., Zhang, J., Wu, H., & Iwahori, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3195039 · Cited by 16

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang, G. F., Gu, C., Li, J., Wang, J., Chen, X., & Zhang, H. (2023) · Drones · doi:10.3390/drones7060380 · Cited by 17

FLASH-and-Prune: Federated Learning for Automated Selection of High-Band mmWave Sectors using Model Pruning

Salehi, B., Roy, D., Gu, J., Dick, C., & Chowdhury, K. R. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3401046 · Cited by 17

JMDC: A joint model and data compression system for deep neural networks collaborative computing in edge-cloud networks

Ding, Y., Fang, W. W., Liu, M., Wang, M., Cheng, Y., & Xiong, N. N. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.11.008 · Cited by 14

Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient

Yu, S., Nguyễn, P., Anwar, A., & Jannesari, A. (2023) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid57682.2023.00038 · Cited by 35

Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use Case

Asad, M., Otoum, S., & Shaukat, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3398213 · Cited by 15

Adaptive Dynamic Pruning for Non-IID Federated Learning.Preprint

Yu, S., Nguyen, P., Anwar, A., & Jannesari, A. (2021) · arXiv (Cornell University) · Cited by 13

Blockchain-Based Self-Sovereign Identity for Federated Learning in Vehicular Networks

Zeydan, E., Blanco, L., Mangues‐Bafalluy, J., Arslan, Ş. S., & Türk, Y. (2023) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm59352.2023.10327817 · Cited by 12

SAM: An Efficient Approach With Selective Aggregation of Models in Federated Learning

Shi, Y., Fan, P., Zhu, Z., Peng, C., Fei, W., & Letaief, K. B. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3373822 · Cited by 12

Application of Federated Learning in Telecommunications and Edge Computing

Mangla, U. (2022) · Springer eBooks · doi:10.1007/978-3-030-96896-0_25 · Cited by 11

FedGroup-Prune: IoT Device Amicable and Training-Efficient Federated Learning via Combined Group Lasso Sparse Model Pruning

Chen, Z., Peng, J., Kang, J., & Niyato, D. T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3457871 · Cited by 10

Revolutionizing Internet of Underwater Things with Federated Learning

Shaheen, M., Farooq, M. S., Umer, T., & Tran, T. A. (2024) · The Springer series in applied machine learning · doi:10.1007/978-3-031-64642-3_12 · Cited by 8

Strategies for Reducing the Communication and Computation Costs in Cross-Silo Federated Learning: A Comprehensive Review

Pais, V., Rao, S., & Muniyal, B. (2025) · IEEE Access · doi:10.1109/access.2025.3573933 · Cited by 10

FLUID: Dynamic Model-Agnostic Federated Learning with Pruning and Knowledge Distillation for Maritime Predictive Maintenance

Kalafatelis, A., Pitsiakou, A., Νομικός, Ν., Tsoulakos, N., Syriopoulos, T. C., & Trakadas, P. T. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13081569 · Cited by 8

Globe2Train: A Framework for Distributed ML Model Training using IoT Devices Across the Globe

Sudharsan, B., Breslin, J. G., & Ali, M. I. (2021) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/swc50871.2021.00024 · Cited by 7

eXtreme Federated Learning (XFL): a layer-wise approach

Mokadem, R. E., Maissa, Y. B., & Akkaoui, Z. E. (2024) · Cluster Computing · doi:10.1007/s10586-023-04242-0 · Cited by 6

Communication-efficient federated learning via personalized filter pruning

Min, Q., Luo, F., Dong, W., Gu, C., & Ding, W. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121030 · Cited by 7

PiPar: Pipeline parallelism for collaborative machine learning

Zhang, Z., Rodgers, P., Kilpatrick, P., Spence, I. T. A., & Varghese, B. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104947 · Cited by 7

Equalized Aggregation for Heterogeneous Federated Mobile Edge Learning

Zhao, Y., Zhang, S., Li, C., Wang, M., Yang, J., & Zhang, M. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3276900 · Cited by 5

Employing Federated Learning for the Implication of Digital Twin

Saeed, F., Shaheen, M., Umer, T., & Farooq, M. S. (2024) · Springer eBooks · doi:10.1007/978-3-031-73679-7_5 · Cited by 5

A dynamic global backbone updating for communication-efficient personalised federated learning

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Structured Sparse Ternary Compression for Convolutional Layers in Federated Learning

Mora, A., Foschini, L., & Bellavista, P. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-spring54318.2022.9860833 · 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

GRACE-FL: Green Resource-Aware Communication-Efficient Federated Learning

Thakur, D., Guzzo, A., Fortino, G., & Das, S. K. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3636489 · Cited by 4

CNN-based prediction of multi-variables for lithium-ion batteries optimized with TinyML and deployed on edge devices

Weng, Y., Guan, W., & Ababei, C. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119508 · Cited by 4

REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments

Desai, H. A., Hilal, A. E., & Eldardiry, H. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-85638-9_3 · Cited by 4

Federated learning for sustainable intrusion detection systems: A review of green computing strategies and future directions

Jithish, J., Mahalingam, N., Wang, B., & Yeo, K. S. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101730 · Cited by 5

Leveraging Blockchain Based Federated Learning for Trustworthy 6G IoMT Image Analysis

Mu, J., Liu, Q., Wang, Z., Yuan, T., Sun, H., & Yu, P. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.013.2300518 · Cited by 4

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

A Cryptocurrency Dual-Offline Payment Method for Payment Capacity Privacy Protection

Si, H., Huang, Y., Li, G., Zhao, Y. B., Qi, Y., Chen, W. P., & Gao, Z. P. (2026) · Electronics · doi:10.3390/electronics15020400 · Cited by 3

ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes

Sánchez, P. M. S., Beltrán, E. T. M., Llamas, M. F., Bovet, G., Pérez, G. M., & Celdrán, A. H. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161228 · Cited by 3

Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study

Raj, A., Sharma, V., Rani, S., Shanu, A. K., & Kumar, N. (2024) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito61523.2024.10522388 · Cited by 3

Federated Learning Communications Optimization Using Sparse Single-Layer Updates

Mokadem, R. E., Maissa, Y. B., & Akkaoui, Z. E. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.05.018 · Cited by 3

SSA: An Effective Secure Scan Architecture Based on Hidden, Randomly Inserted Keys and PUF

Wang, W., Wu, Z., Chen, J., Liu, P., Cai, S., & Xiong, N. N. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3566412 · 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

Proceedings of the 3rd ACM MobiCom Workshop on Integrated Sensing and Communications Systems

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SMART-FL: Single-Shot Merged Adaptive Resource-Aware Tensor-Fusion for Efficient Federated Learning in Heterogeneous Cross-Silo Environments

Pais, V., Rao, S., & Muniyal, B. (2025) · IEEE Access · doi:10.1109/access.2025.3570610 · Cited by 2

Using AI and IoT at the Edge of the network

Lakrouni, S., Sebgui, M., & Bah, S. (2022) · International Conference on Optimization and Applications (ICOA) · doi:10.1109/icoa55659.2022.9934603 · Cited by 2

Review and Comparative Evaluation of Resource-Adaptive Collaborative Training for Heterogeneous Edge Devices

Radovič, B., Canini, M., & Pejović, V. (2024) · ACM Transactions on Modeling and Performance Evaluation of Computing Systems · doi:10.1145/3708983 · Cited by 2

Communication-Efficient Heterogeneous Federated Dropout in Cross-device Settings

Bellavista, P., Foschini, L., & Mora, A. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685710 · Cited by 2

Adaptive Ratio-Based-Threshold Gradient Sparsification Scheme for Federated Learning

Kong, J. M., & Sousa, E. S. (2023) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc58260.2023.10323644 · Cited by 2

Server Access Pattern Analysis Based on Weblogs Classification Methods

Dubey, S. M., Tiwari, G., & Narwaria, P. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-8646-0_16 · Cited by 2

A Channel Aggregation Based Dynamic Pruning Method in Federated Learning

Chen, Z., Tan, X., Zhou, Z., & Zhou, Y. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437223 · Cited by 2

OPALA: Optimized Pruning Adaptive Learning Approach for Federated Learning Scenarios

Veiga, R., Morais, R., Bustincio, R., Bastos, L., Rosário, D., Sargento, S., & Cerqueira, E. (2025) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc65549.2025.11325972 · Cited by 1

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

Li, N., Wang, X., Du, X., Wang, C., Cai, P., & Wang, T. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11322956 · Cited by 1

PECC: Position Encoding Coordinate Classification System Design for Human Pose Estimation

Zhang, T., Zhang, Y., Wu, Q., Chen, Y.-C., & Xiong, N. N. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3649204 · Cited by 1

Transformers for Internet of Things Security and Communications: A Comprehensive Survey

Salman, M., Saeed, N., & Khurshid, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3717847 · Cited by 1

The Necessity to Adopt Big Data Technologies for Efficient Performance Evaluation in the Modern Era

Gupta, S., & Mishra, R. K. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-9113-3_45 · Cited by 1

Reviewing and Discussing Graph Reduction in Edge Computing Context

Garmendia-Orbegozo, A., Núñez-González, J. D., & Antón, M. Á. (2022) · Computation · doi:10.3390/computation10090161 · Cited by 1

NoSQL Cloud based Bigdata technologies for efficient performance evaluation in the modern era

Gupta, S., & Agrawal, K. (2022) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn56167.2022.10008325 · Cited by 1

Decentralized Communication-assisted Sensing based on Federated Learning Framework for IIoT

Jin, J., Jiao, Z., Mu, J., Lv, W., Tang, Y., & Yuan, T. (2023) · ACM MobiCom Workshop on Integrated Sensing and Communications Systems (ISACom) · doi:10.1145/3615984.3616507 · Cited by 1

WeightRelay: Efficient Heterogeneous Federated Learning on Time Series

Tang, W., & Long, G. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8388-9_11 · Cited by 1

Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs

Taya, A., Nishiyama, Y., & Sezaki, K. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437801 · Cited by 1

Study on federated learning model with pruning, optimal quantization and selective updating for IOT and big data analytics

Ellaji, C., Ponmagal, R. S., & Saritha, V. (2024) · AIP conference proceedings · doi:10.1063/5.0217389 · Cited by 1

HaLo-FL: Hardware-Aware Low-Precision Federated Learning

Venkatesha, Y., Bhattacharjee, A., Moitra, A., & Panda, P. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546513 · Cited by 1

Indeterministic Data Collection in UAV-Assisted Wide and Sparse Wireless Sensor Network

Du, Y. Y., Hao, J., Chen, Z., & Guo, Y. (2024) · Sensors · doi:10.3390/s24196496 · Cited by 1

Pipar: Pipeline Parallelism for Collaborative Machine LearningPreprint

Zhang, Z., Rodgers, P., Kilpatrick, P., Spence, I. T. A., & Varghese, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4697027 · Cited by 1

Utilizing Stacked Ensemble AI Algorithm for Comprehensive Water Quality Analysis and Health Assessment in Complex Aquatic SystemsPreprint

Muhammad Mahfuzur, D., Syed Hamid Hussain Kala, T., Al-Otaibi, S. T., & Khan, I. (2024) · Research Square · doi:10.21203/rs.3.rs-4113265/v1 · Cited by 1

A Voting-Based Poisoning Attack to Federated Learning with Quantization

Ma, X., Wei, L., Zhang, B., Wang, Y., Zhang, C., & Li, Y. (2022) · International Conference on Hot Information-Centric Networking (HotICN) · doi:10.1109/hoticn57539.2022.10036222

Lempel-Ziv-Welch (LZW) based Horizontally Scalable Route Prediction

Chaturvedi, S., Nagpal, T., & Tiwari, V. S. (2022) · International Conference on Futuristic Technologies (INCOFT) · doi:10.1109/incoft55651.2022.10094463

FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive Compression

Ying, C., Li, B., & Li, B. (2023) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass58611.2023.00013

Security Intelligence Enhanced by Blockchain Data Transitions and Effective Handover Authentication

Raja, V. A. A., & Senthamarai, C. (2024) · Journal of Machine and Computing · doi:10.53759/7669/jmc202404035

Adaptive Lazily Aggregated Quantized Algorithm Based on Federated Self-Supervision

Ding, Y. (2024) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2024.070509

Towards Superior Pruning Performance in Federated Learning with Discriminative Data

Yinan, Y. A. N. G. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024pap0002

Towards Efficient Federated Learning Framework via Selective Aggregation of Models

Shi, Y., Fan, P., Zhu, Z., Peng, C., Wang, F., & Letaief, K. B. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615921

Efficient Federated Learning Through Distributed Model Pruning

Islam, M. M., & Alawad, M. A. (2024) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi61997.2024.00038

Federated Learning

Willburger, L. (2024) · Financial innovation and technology · doi:10.1007/978-3-031-66047-4_5

Optimisation of a Convolutional Neural Network Based Model for Thermal Image Recognition of Faces

Lu, Y., Su, J., & Shi, Y. (2024) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics62053.2024.10796228

Personalized Federated Learning by Model Pruning via Batch Normalization Layers

Wang, A., & Gong, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2681-6_7

The “Server Access Pattern Analysis” Based on Different “Weblogs Classification” Methods

Dixit, A., Tiwari, P. N., Tiwari, K. P., & Keer, V. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-0476-0_15

Overview of Quantum Communication Based on the Ocean

Zhang, Y., Liang, W., Huang, S., Zhang, H., & Xiong, N. N. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-5693-6_12

QL-PGD: An efficient defense against membership inference attack

Pham, T. D., Nguyen, B. D., Mai, S. T., & Ta, V. C. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104095

Federated Learning for UAV Perception Task : A SurveyPreprint

Bardes, Y., Soubra, H., Noumir, Z., & Ramdane-Chérif, A. (2025) · doi:10.36227/techrxiv.174906148.89641328/v1 · Cited by 1

Study on federated learning model with pruning, optimal quantization and selective updating for IOT and big data analytics

Ellaji, C., Ponmagal, R. S., & Saritha, V. (2024) · AIP conference proceedings · doi:10.1063/5.0217389 · Cited by 1

FedQNN: A Computation–Communication-Efficient Federated Learning Framework for IoT With Low-Bitwidth Neural Network Quantization

Ji, Y., & Chen, L. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3213650 · Cited by 61

IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization

Prakash, P., Ding, J., Chen, R., Qin, X., Shu, M., Cui, Q., Guo, Y., & Pan, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3145865 · Cited by 98

QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model QuantisationPreprint

Herath, C., Rahulamathavan, Y., Silva, V. D., & Lambotharan, S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2603.17507

IDMN-SP: Improved Deep Maxout Network with Adaptive Structured Pruning for Resource-Efficient Federated Learning in IoT Big Data Analytics

Ellaji, C., Ponmaga, R. S., & Saritha, V. (2026) · International Academic Journal of Science and Engineering · doi:10.71086/iajse/v13i3/iajse13122