A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Learning Multiple Layers of Features from Tiny Images

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

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

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772

The multimodal brain tumor image segmentation benchmark (BRATS)

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322

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

The MovieLens Datasets: History and Context

Harper, F., & Konstan, J. (2016) · TIIS · doi:10.1145/2827872 · Cited by 5,938

Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318

Practical Secure Aggregation for Privacy-Preserving Machine Learning

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956

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

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565

Towards Federated Learning at Scale: System Design

Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334

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

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018) · Scientific Data · doi:10.1038/sdata.2018.178 · Cited by 1,993

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

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

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

Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563

How to backdoor federated learning

Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407

Federated Multi-Task LearningPreprint

Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10467 · Cited by 1,282

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

Federated Learning via Over-the-Air Computation

Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2018) · arXiv · doi:10.48550/arxiv.1812.11750 · Cited by 1,090

Federated Learning via Over-the-Air Computation

Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084 · cited 1×

Differentially Private Federated Learning: A Client Level Perspective

Geyer, R. C., Klein, T., & Nabi, M. (2017) · arXiv · doi:10.48550/arxiv.1712.07557 · Cited by 1,052

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800286 · Cited by 1,030 · cited 3×

A Hybrid Approach to Privacy-Preserving Federated Learning

Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., & Zhou, Y. (2019) · Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3338501.3357370 · Cited by 1,029

FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005

Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine

Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35395-6_30 · Cited by 998

Federated learning of predictive models from federated Electronic Health Records

Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.007 · Cited by 997 · cited 2×

Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory

Kang, J., Xiong, Z., Niyato, D. T., Xie, S., & Zhang, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2940820 · Cited by 961

Federated Learning over Wireless Networks: Optimization Model Design and Analysis

Tran, N. H., Bao, W., Zomaya, A. Y., Nguyen, M. N. H., & Hong, C. (2019) · IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737464 · Cited by 943

Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., & Qi, H. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737416 · Cited by 881

Learning Differentially Private Recurrent Language Models

McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2017) · arXiv · doi:10.48550/arxiv.1710.06963 · Cited by 849

Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges

Niknam, S., Dhillon, H. S., & Reed, J. H. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900461 · Cited by 820

Analyzing Federated Learning through an Adversarial Lens

Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. (2018) · arXiv · doi:10.48550/arxiv.1811.12470 · Cited by 703

Applied Federated Learning: Improving Google Keyboard Query Suggestions

Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., & Beaufays, F. (2018) · arXiv · doi:10.48550/arxiv.1812.02903 · Cited by 684

SecureBoost: A Lossless Federated Learning Framework

Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q. (2021) · IEEE Intelligent Systems · doi:10.1109/mis.2021.3082561 · Cited by 669

SecureBoost: A Lossless Federated Learning Framework

Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q. (2019) · arXiv · doi:10.48550/arxiv.1901.08755 · Cited by 668

DÏoT: A Federated Self-learning Anomaly Detection System for IoT

Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663

Agnostic Federated LearningPreprint

Mohri, M., Sivek, G., & Suresh, A. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00146 · Cited by 652

A secure federated transfer learning framework

Liu, Y., Kang, Y., Xing, C., Chen, T., & Yang, Q. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988525 · Cited by 645

Scheduling Policies for Federated Learning in Wireless Networks

Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2944169 · Cited by 640

Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609

Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

Chen, Y., Sun, X., & Jin, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2953131 · Cited by 597

Privacy-Preserving Federated Brain Tumour Segmentation

Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578

Data driven prediction models of energy use of appliances in a low-energy house

Candanedo, L. M., Feldheim, V., & Deramaix, D. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.01.083 · Cited by 559

Making Gradient Descent Optimal for Strongly Convex Stochastic OptimizationPreprint

Rakhlin, A., Shamir, O., & Sridharan, K. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1109.5647 · Cited by 519

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 494

Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

Xu, J., & Wang, H. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3031503 · Cited by 480

HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

Luo, S., Chen, X., Wu, Q., Zhou, Z., & Yu, S. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3003744 · Cited by 472

Practical Secure Aggregation for Federated Learning on User-Held DataPreprint

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04482 · Cited by 468

Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation SystemPreprint

Ammad-ud-din, M., Ivannikova, E., Khan, S. A., Oyomno, W., Fu, Q., Tan, K. E., & Flanagan, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09888 · Cited by 464

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103291 · Cited by 437

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., & Thorne, B. (2017) · arXiv · doi:10.48550/arxiv.1711.10677 · Cited by 431

Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

Abad, M. S. H., Ozfatura, E., GUndUz, D., & Ercetin, O. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054634 · Cited by 424

Protection Against Reconstruction and Its Applications in Private Federated LearningPreprint

Bhowmick, A., Duchi, J. C., Freudiger, J., Kapoor, G., & Rogers, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00984 · Cited by 420

Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?

Rahman, S. A., Tout, H., Talhi, C., & Mourad, A. (2020) · IEEE Network · doi:10.1109/mnet.011.2000286 · Cited by 414

Mitigating Sybils in Federated Learning Poisoning

Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2018) · arXiv · doi:10.48550/arxiv.1808.04866 · Cited by 385

TiFL: A Tier-based Federated Learning System

Chai, Z., Ali, A. Z. E., Zawad, S., Truex, S., Anwar, A., Baracaldo, N., Zhou, Y., Ludwig, H., Yan, F., & Cheng, Y. (2020) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3369583.3392686 · Cited by 352

A Performance Evaluation of Federated Learning Algorithms

Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M. (2018) · Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning · doi:10.1145/3286490.3286559 · Cited by 328

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018) · Applied Sciences · doi:10.3390/app8122663 · Cited by 323

On Safeguarding Privacy and Security in the Framework of Federated Learning

Ma, C., Li, J., Ding, M., Yang, H. H., Shu, F., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Network · doi:10.1109/mnet.001.1900506 · Cited by 318

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Caldas, S., Konečny, J., McMahan, H. B., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.07210 · Cited by 312

Federated Learning for Keyword Spotting

Leroy, D., Coucke, A., Lavril, T., Gisselbrecht, T., & Dureau, J. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683546 · Cited by 291

Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles

Li, L., Ota, K., & Dong, M. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2822762 · Cited by 287

Real-time Credit Card Fraud Detection Using Machine Learning

Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., & Kuruwitaarachchi, N. (2019) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence.2019.8776942 · Cited by 268

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

Kang, J., Xiong, Z., Niyato, D., Yu, H., Liang, Y.-C., & Kim, D. I. (2019) · 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS) · doi:10.1109/vts-apwcs.2019.8851649 · Cited by 255

Earthquake Prediction Based on Spatio-Temporal Data Mining: An LSTM Network Approach

Wang, Q., Guo, Y., Yu, L., & Li, P. (2017) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2017.2699169 · Cited by 250

Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs

Challita, U., Ferdowsi, A., Chen, M., & Saad, W. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1800155 · Cited by 246

Reinforcement learning for mapping instructions to actions

Branavan, S. R. K., Chen, H., Zettlemoyer, L., & Barzilay, R. (2009) · doi:10.3115/1687878.1687892 · Cited by 245

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

Liu, B., Wang, L., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931179 · Cited by 242

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852172 · Cited by 240

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

Accelerating DNN Training in Wireless Federated Edge Learning Systems

Ren, J., Yu, G., & Ding, G. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036971 · Cited by 227

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 225

Threats to federated learning A survey

Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv · doi:10.48550/arxiv.2003.02133 · Cited by 221

Cognitive Smart Healthcare for Pathology Detection and Monitoring

Amin, S. U., Hossain, M. S., Muhammad, G., Alhussein, M. A., & Rahman, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2891390 · Cited by 218

Device Scheduling with Fast Convergence for Wireless Federated Learning

Shi, W., Zhou, S., & Niu, Z. (2020) · ICC 2020 - 2020 IEEE International Conference on Communications (ICC) · doi:10.1109/icc40277.2020.9149138 · Cited by 216

A training-integrity privacy-preserving federated learning scheme with trusted execution environment

Chen, Y., Luo, F., Li, T., Xiang, T., Liu, Z., & Li, J. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.037 · Cited by 194

Analyzing Federated Learning through an Adversarial Lens

Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. B. (2019) · International Conference on Machine Learning · Cited by 188

Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things

Ren, J., Wang, H., Hou, T., Zheng, S., & Tang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2919736 · Cited by 185

Differential privacy-enabled federated learning for sensitive health data

Choudhury, O., Gkoulalas-Divanis, A., Salonidis, T., Sylla, I., Park, Y., Hsu, G., & Das, A. (2019) · arXiv · doi:10.48550/arxiv.1910.02578 · Cited by 184

A Low-Power Processor With Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals

Lee, K.-H., & Verma, N. (2013) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2013.2253226 · Cited by 177

Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge TransferPreprint

Chang, H., Shejwalkar, V., Shokri, R., & Houmansadr, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11279 · Cited by 176

FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things

Yin, B., Yin, H., Wu, Y., & Jiang, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2966778 · Cited by 149

One-Shot Federated LearningPreprint

Guha, N., Talwalkar, A., & Smith, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11175 · Cited by 137

Privacy-preserving Heterogeneous Federated Transfer Learning

Gao, D., Liu, Y., Huang, A., Ju, C., Yu, H., & Yang, Q. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005992 · Cited by 133

Two-Stream Federated Learning: Reduce the Communication Costs

Yao, X., Huang, C., & Sun, L. (2018) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2018.8698609 · Cited by 123

Machine Learning on Mobile: An On-device Inference App for Skin Cancer Detection

Dai, X., Spasić, ‪., Meyer, B., Chapman, S., & Andrès, F. (2019) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2019.8795362 · Cited by 123

The development of intuitive knowledge classifier and the modeling of domain dependent data

Kahraman, H. T., Sağıroğlu, Ş., & Çolak, İ. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.08.009 · Cited by 118

Ad Hoc Vehicular Fog Enabling Cooperative Low-Latency Intrusion Detection

Mourad, A., Tout, H., Wahab, O. A., Otrok, H., & Dbouk, T. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3008488 · Cited by 110

μLayer

Kim, Y., Kim, J., Chae, D., Kim, D., & Kim, J. (2019) · European Conference on Computer Systems · doi:10.1145/3302424.3303950 · Cited by 101

Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment

Sami, H., & Mourad, A. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2963643 · Cited by 98

Federated Learning Of Out-Of-Vocabulary Words

Chen, M., Mathews, R., Ouyang, T., & Beaufays, F. (2019) · arXiv · doi:10.48550/arxiv.1903.10635 · Cited by 97

Performance Optimization of Federated Learning over Wireless Networks

Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. R. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013160 · Cited by 95

On-Device Federated Learning via Blockchain and its Latency Analysis.

Kim, H., Park, J., Bennis, M., & Kim, S. (2018) · Cited by 94

Conversational Contextual Cues: The Case of Personalization and History for Response RankingPreprint

Al‐Rfou, R., Pickett, M., Snaider, J., Sung, Y.-H., Strope, B. P., & Kurzweil, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00372 · Cited by 93

IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning

Aivodji, U. M., Gambs, S., & Martin, A. (2019) · 2019 IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2019.00041 · Cited by 93

Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System.Preprint

Zhao, Y., Zhao, J., Jiang, L., Tan, R., & Niyato, D. T. (2019) · arXiv (Cornell University) · Cited by 83

A Novel Ad-Hoc Mobile Edge Cloud Offering Security Services Through Intelligent Resource-Aware Offloading

Dbouk, T., Mourad, A., Otrok, H., Tout, H., & Talhi, C. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2939221 · Cited by 79

Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionPreprint

Li, Q., Wen, Z., & He, B. (2019) · arXiv (Cornell University) · Cited by 75

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyPreprint

Gao, D., Ju, C., Wei, X., Liu, Y., Chen, T., & Yang, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05784 · Cited by 68

Resource-Efficient and Convergence-Preserving Online Participant Selection in Federated Learning

Jin, Y., Jiao, L., Qian, Z., Zhang, S., Lu, S., & Wang, X. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00049 · Cited by 68

Fadl Federated-autonomous deep learning for distributed electronic health record

Liu, D., Miller, T., Sayeed, R., & Mandl, K. D. (2018) · arXiv · doi:10.48550/arxiv.1811.11400 · Cited by 65

Update Aware Device Scheduling for Federated Learning at the Wireless Edge

Amiri, M., Gündüz, D., Kulkarni, S., & Poor, H. (2020) · International Symposium on Information Theory · doi:10.1109/isit44484.2020.9173960 · Cited by 62

Self Organizing Federated Learning Over Wireless Networks: A Socially Aware Clustering Approach

Khan, L. U., Alsenwi, M., Han, Z., & Hong, C. S. (2020) · International Conference on Information Networking · doi:10.1109/icoin48656.2020.9016505 · Cited by 61

On lightweight privacy-preserving collaborative learning for internet-of-things objects

Jiang, L., Tan, R., Lou, X., & Lin, G. (2019) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3302505.3310070 · Cited by 56

Edge-Assisted Hierarchical Federated Learning with Non-IID DataPreprint

Liu, L., Zhang, J., Song, S., & Letaief, K. B. (2019) · arXiv (Cornell University) · Cited by 52

Federated Reinforcement Learning

Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., & Yu, H. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01585-4_9 · Cited by 44

Collaborative Learning on the Edges: A Case Study on Connected Vehicles

Lu, S., Yao, Y., & Shi, W. (2019) · Cited by 43

Hybrid-FL: Cooperative Learning Mechanism Using Non-IID Data in Wireless Networks.Preprint

Yoshida, N., Nishio, T., Morikura, M., Yamamoto, K., & Yonetani, R. (2019) · arXiv (Cornell University) · Cited by 37

Towards Effective Device-Aware Federated Learning

Anelli, V. W., Deldjoo, Y., Noia, T. D., & Ferrara, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35166-3_34 · Cited by 37

A novel on-demand vehicular sensing framework for traffic condition monitoring

AbdulRahman, S., Mourad, A., Barachi, M. E., & Orabi, W. A. (2018) · Vehicular Communications · doi:10.1016/j.vehcom.2018.03.001 · Cited by 35

An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning

Li, H., & Han, T. (2019) · DCC · doi:10.1109/dcc.2019.00101 · Cited by 35

Federated Reinforcement Learning

(2024) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789811292552_0013 · Cited by 33

Fast Uplink Grant for NOMA: A Federated Learning Based Approach

Habachi, O., Adjif, M. A., & Cances, J.-P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58008-7_8 · Cited by 23

CO-OP: Cooperative Machine Learning from Mobile Devices

Wang, Y. (2017) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r32805c45 · Cited by 22

AI Gauge: Runtime Estimation for Deep Learning in the Cloud

Dube, P., Suk, T., & Wang, C. (2019) · Proceedings · doi:10.1109/sbac-pad.2019.00035 · Cited by 22

An Infrastructure-Assisted Crowdsensing Approach for On-Demand Traffic Condition Estimation

AbdulRahman, S., Mourad, A., & Barachi, M. E. (2019) · IEEE Access · doi:10.1109/access.2019.2953002 · Cited by 21

Machine Learning for All: A More Robust Federated Learning Framework

Chamatidis, I., & Georgios, S. (2019) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0007571705440551 · Cited by 20

Smart Classrooms Aided by Deep Neural Networks Inference on Mobile Devices

Pacheco, A., Flores, E., Sanchez, R., & Almanza-Garcia, S. (2018) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2018.8500260 · Cited by 18

Scheduling for Cellular Federated Edge Learning With Importance and Channel Awareness

Ren, J., He, Y.-H., Wen, D.-Z., Yu, G., Huang, K., & Guo, D. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3015671 · Cited by 14

Using Machine Learning to Address Customer Privacy Concerns: An Application with Click-stream DataPreprint

Yoo, H., Yao, S., Sun, L., & Du, X. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3314787 · Cited by 8

Federated Learning for Intrusion Detection Systems in Medical Cyber-Physical Systems

Schneble, W. (2018) · ResearchWorks at the University of Washington (University of Washington) · Cited by 4

Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting PhasesPreprint

Huang, H., Lin, K., Guo, S., Zhou, P., & Zheng, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00577 · Cited by 4

Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892

Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems

Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3058573 · Cited by 506

Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

Ghimire, B., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150363 · Cited by 520

A state-of-the-art survey on solving non-IID data in Federated Learning

Ma, X., Zhu, J., Lin, Z., Chen, S., & Qin, Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.003 · Cited by 435

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342

Federated learning: Challenges and future work

Hossain, M. B., & Rahman, R. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.15.2.0711 · Cited by 5

Towards_Scalable_and_Secure_Federated_Learning__Key_Issues_and_Future_Research.pdf

Daniels, M., Gallus, S., & Wood, R. (2025) · Figshare · doi:10.6084/m9.figshare.28238567

Federated Learning: A Survey on Privacy-Preserving Collaborative IntelligencePreprint

Rahman, R. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2504.17703 · Cited by 3

Federated Learning With Deep Learning: A Comprehensive Survey

Mnkash, S. H., Alawy, F. A. A., & Ali, I. T. (2026) · Iraqi Journal of Computer Communication Control and System Engineering · doi:10.33103/2617-3352.1524

Federated Learning: Healthcare, Security, Challenges, and ThreatsPreprint

Sumanth, N., Heggere, S. B., Prathiksha, E., Shetty, C. A., Nikhitha, H. R., & Soundarya, B. C. (2023) · Research Square · doi:10.21203/rs.3.rs-3223963/v1