Layer-Based Communication-Efficient Federated Learning with Privacy Preservation

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works

Federated learning: Challenges, methods, and future directions

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · see citing works

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv · doi:10.48550/arxiv.1708.07747 · Cited by 6,108 · see citing works

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 · see citing works

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875 · see citing works

Exploiting Unintended Feature Leakage in Collaborative Learning

Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734 · see citing works

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 · see citing works

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 · see citing works

Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

Yu, H., Yang, S., & Zhu, S.-H. (2018) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015693 · Cited by 696 · see citing works

Agnostic Federated LearningPreprint

Mohri, M., Sivek, G., & Suresh, A. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00146 · Cited by 619 · see citing works

CMFL: Mitigating Communication Overhead for Federated Learning

Wang, L., Wang, W., & Li, B. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00099 · Cited by 492 · see citing works

LDP-Fed: federated learning with local differential privacy

Truex, S., Liu, L., Chow, K.-H., Gursoy, M. E., & Wei, W. (2020) · Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking · doi:10.1145/3378679.3394533 · Cited by 435 · see citing works

Image-Based malware classification using ensemble of CNN architectures (IMCEC)

Vasan, D., Alazab, M., Wassan, S., Safaei, B., & Zheng, Q. (2020) · Computers & security · doi:10.1016/j.cose.2020.101748 · Cited by 374 · see citing works

Blockchain-Based Reliable and Efficient Certificateless Signature for IIoT Devices

Wang, W., Xu, H., Alazab, M., Gadekallu, T. R., Han, Z., & Su, C. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3084753 · Cited by 285 · see citing works

LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy

Sun, L., Qian, J., & Chen, X. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/217 · Cited by 267 · see citing works

ATOMO: Communication-efficient Learning via Atomic SparsificationPreprint

Wang, H., Sievert, S., Charles, Z., Liu, S., Wright, S. J., & Papailiopoulos, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04090 · Cited by 247 · see citing works

Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning

Phan, N., Wu, X., Hu, H., & Dou, D. (2017) · Proceedings · doi:10.1109/icdm.2017.48 · Cited by 233 · see citing works

Federated Learning for Emoji Prediction in a Mobile Keyboard

Ramaswamy, S., Mathews, R., Rao, K., & Beaufays, F. (2019) · arXiv · doi:10.48550/arxiv.1906.04329 · Cited by 231 · see citing works

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

Silva, S., Gutman, B. A., Romero, E., Thompson, P. M., Altmann, A., & Lorenzi, M. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759317 · Cited by 212 · see citing works

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. (2019) · arXiv · doi:10.48550/arxiv.1905.06731 · Cited by 212 · see citing works

Hand gesture classification using a novel CNN-crow search algorithm

Gadekallu, T. R., Alazab, M., Kaluri, R., Maddikunta, P. K. R., Bhattacharya, S., Lakshmanna, K., & Parimala, M. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00324-x · Cited by 184 · see citing works

An Overview of Privacy in Machine LearningPreprint

Cristofaro, E. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.08679 · Cited by 91 · see citing works

Geryon: Accelerating Distributed CNN Training by Network-Level Flow Scheduling

Wang, S., Li, D., & Geng, J. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155282 · Cited by 60 · see citing works

COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy

Lian, Z., Wang, W., & Su, C. (2021) · ICC 2021 - IEEE International Conference on Communications · doi:10.1109/icc42927.2021.9500632 · Cited by 58 · see citing works

LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential PrivacyPreprint

Sun, L., Qian, J., & Chen, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15789 · Cited by 30 · see citing works

Federated Learning: Challenges, Methods, and Future Directions

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2019) · arXiv · doi:10.48550/arxiv.1908.07873

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Javed, A. R., Shahzad, F., Rehman, S. U., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022) · Cities · doi:10.1016/j.cities.2022.103794 · Cited by 645 · see citing works

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works

Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief Survey

Xie, Q., Jiang, S., Jiang, L., Huang, Y., Zhao, Z., Khan, S., Dai, W., Liu, Z., & Wu, K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3382875 · Cited by 209 · see citing works

Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT

Singh, P., Gaba, G. S., Kaur, A., Hedabou, M., & Gurtov, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3186250 · Cited by 103 · see citing works

A Privacy-Preserving Medical Data Sharing Scheme Based on Blockchain

Xu, G., Chen, Q., Dong, W., Gong, L., Liu, S., Chen, S., Liu, J., & Zheng, X. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3203577 · Cited by 90 · see citing works

Blockchain-Based Privacy Preservation Scheme for Misbehavior Detection in Lightweight IoMT Devices

Rahmadika, S., Astillo, P. V., Choudhary, G., Duguma, D. G., Sharma, V., & You, I. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3187037 · Cited by 80 · see citing works

Blockchain-Based Decentralized and Lightweight Anonymous Authentication for Federated Learning

Fan, M., Zhang, Z., Li, Z., Sun, G., Yu, H., & Guizani, M. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3265366 · Cited by 73 · see citing works

Federal learning edge network based sentiment analysis combating global COVID-19

Liang, W., Chen, X., Huang, S., Xiong, G., Yan, K., & Zhou, X. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.03.009 · Cited by 49 · see citing works

Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning

Almadhor, A. S., Sampedro, G. A., Abisado, M., Abbas, S., Kim, Y., Khan, M. A., Baili, J., & Cha, J. (2023) · Sensors · doi:10.3390/s23083984 · Cited by 45 · see citing works

A secure and efficient framework for internet of medical things through blockchain driven customized federated learning

Mazid, A., Kirmani, S., Abid, M., & Pawar, V. (2025) · Cluster Computing · doi:10.1007/s10586-024-04896-4 · Cited by 36 · see citing works

Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection

Ahmed, U., Lin, J. C., & Srivastava, G. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172269 · Cited by 31 · see citing works

A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues

Deepanshi, D., Budhiraja, I., Garg, D., Kumar, N., & Sharma, R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.10.013 · Cited by 24 · see citing works

Communication-Efficient Personalized Federated Learning With Privacy-Preserving

Wang, Q., Chen, S., & Wu, M. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3323129 · Cited by 19 · see citing works

AWDP-FL: An Adaptive Differential Privacy Federated Learning Framework

Chen, Z., Zheng, H. Y., & Liu, G. (2024) · Electronics · doi:10.3390/electronics13193959 · Cited by 15 · see citing works

Online learning resource recommendation method based on multi-similarity metric optimization under the COVID-19 epidemic

Wang, J., Jiang, S., & Ding, J. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.04.024 · Cited by 15 · see citing works

AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects

Huang, H., Dai, D., Guo, L., Xue, S., & Wu, H. (2023) · Sustainability · doi:10.3390/su151612332 · Cited by 14 · see citing works

Federated learning‐based private medical knowledge graph for epidemic surveillance in internet of things

Wu, X., Gao, J., Bilal, M., Dai, F., Xu, X., Qi, L., & Dou, W. (2023) · Expert Systems · doi:10.1111/exsy.13372 · Cited by 13 · see citing works

Client selection and resource scheduling in reliable federated learning for UAV-assisted vehicular networks

Zhao, H., Geng, L., Feng, W., & Changming, Z. H. O. U. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.06.023 · Cited by 11 · see citing works

ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection

Guo, Z., You, L., Liu, S., He, J., & Zuo, B.-R. (2023) · Mathematics · doi:10.3390/math11081867 · Cited by 10 · see citing works

Privacy-Enhanced Federated Generative Adversarial Networks for Internet of Things

Zeng, Q., Zhou, L., Lian, Z., Huang, H., & Kim, J. (2022) · The Computer Journal · doi:10.1093/comjnl/bxac060 · Cited by 10 · see citing works

ENASFL: A Federated Neural Architecture Search Scheme for Heterogeneous Deep Models in Distributed Edge Computing Systems

Zhang, Z., Liu, Z., Zhao, Y., Qiu, C., Zhang, C., & Wang, X. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3344850 · Cited by 8 · see citing works

Joint Layer Selection and Differential Privacy Design for Federated Learning Over Wireless Networks

Ding, Y., Shang, W., Yang, Y., Ding, W., & Shikh‐Bahaei, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3452549 · Cited by 4 · see citing works

DGDPFL: Dynamic Grouping and Privacy Budget Adjustment for Federated Learning in Networked Service Management

Han, D., & Zhi, Q. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3640713 · Cited by 3 · see citing works

Privacy Preservation and Identity Tracing Prevention in AI-Driven Eye Tracking for Interactive Learning Environments

Rehman, A., Dæhlen, A., Heldal, I., & Lin, J. C. (2025) · IEEE Access · doi:10.1109/access.2025.3645115 · Cited by 2 · see citing works

Accelerating model convergence in federated learning with layer-wise adaptive weight aggregation

Khan, A. N., Rizwan, A., Ahmad, R., Khan, Q. W., Li, X., & kim, D. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115676 · Cited by 2 · see citing works

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

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

Privacy-stealing Approach in Distributed IoMT Systems

Wang, H., Zhao, L., Qiu, C., Lian, Z., & Su, C. (2023) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc60832.2023.00051

Layer Selection for Secure Federated Learning in Wireless Large AI Model (WLAM)

Ding, Y., Yang, Z., & Shikh‐Bahaei, M. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11100604

PPTADI: A privacy-preserving training and accelerated distributed inference framework in low-resource AIoT scenarios

Meng, H., Hu, Y., Liu, K., & Guan, J. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108084

MPCSFL: A Privacy-Preserving Split Federated Learning Framework in Edge Network

Guan, J., Meng, H., Hu, Y., Wang, P., & Liu, K. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209514

Optimization-Oriented Deep Learning Algorithm for Automatic Digital Media Animation Generation with Enhanced Communication Efficiency

Liu, Y., & Li, H. (2025) · International Conference on Electronic Circuits and Signaling Technologies (ICECST) · doi:10.1109/icecst66106.2025.11307360

Adversarial Robustness of Federated Learning in Healthcare

Saranya, P., Saranya, A., Janardhanan, J., Shukla, G., Iyer, R., & Panthakkan, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6224-3.ch007

COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy

Lian, Z., Wang, W., & Su, C. (2021) · ICC 2021 - IEEE International Conference on Communications · doi:10.1109/icc42927.2021.9500632 · Cited by 58 · see citing works

CLFLDP: Communication-efficient layer clipping federated learning with local differential privacy

Chen, S., Yang, J., Wang, G., Wang, Z., Yin, H., & Feng, Y. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103067 · Cited by 32 · see citing works

Efficient Federated Learning Method FedLayerPrune Based on Layer Adaptive Pruning

He, W., Cao, H. Y., Zhang, J., & Yang, D. (2026) · Electronics · doi:10.3390/electronics15051049

Efficient privacy-preserving federated learning with local differential privacy

Yin, C., & Zeng, Q. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112351

Privacy-preserving federated learning approach based on Hensel’s compression and differential privacy

Ouadrhiri, A. E., Phung, P. H., Nasser, N., Boudine, B., & Abdelhadi, A. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00236-z · Cited by 2 · see citing works