A survey on federated learning

Advances in neural information processing systems 7

Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · 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

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Proceedings of the 25th international conference on Machine learning - ICML '08

(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · see citing works

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 · see citing works

International Conference on Learning Representations (ICLR 2013)

松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286 · see citing works

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 · see citing works

Blockchain challenges and opportunities: a survey

Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018) · International Journal of Web and Grid Services · doi:10.1504/ijwgs.2018.095647 · Cited by 3,651 · see citing works

Differential Privacy: A Survey of Results

Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542 · see citing works

International Conference on Machine Learning (ICML)-2005

津田, 宏. (2006) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 2,558 · see citing works

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 · see citing works

Artificial intelligence and statistics

Yu, B., & Kumbier, K. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700813 · Cited by 2,329 · see citing works

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 · see citing works

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 · see citing works

Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

Phong, L. T., Aono, Y., Hayashi, T., Wang, L., & Moriai, S. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2787987 · Cited by 1,662 · see citing works

Blockchain challenges and opportunities: a survey

Wang, H., Zheng, Z., Xie, S., Dai, H., & Chen, X. (2018) · International Journal of Web and Grid Services · doi:10.1504/ijwgs.2018.10016848 · Cited by 1,582 · see citing works

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 · see citing works

Privacy in the age of medical big data

Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522 · see citing works

2014 IEEE Symposium on Security and Privacy

(2014) · doi:10.1109/sp16517.2014 · Cited by 1,388 · see citing works

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 · see citing works

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

(2015) · doi:10.1109/icassp17257.2015 · Cited by 1,263 · see citing works

Scaling distributed machine learning with the parameter server

Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A. A., Josifovski, V., Long, J. E., Shekita, E. J., & Su, B.-Y. (2014) · Operating Systems Design and Implementation · doi:10.5555/2685048.2685095 · Cited by 1,178 · see citing works

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 · see citing works

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 · see citing works

Privacy preserving association rule mining in vertically partitioned data

Vaidya, J., & Clifton, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775142 · Cited by 1,001 · see citing works

Federated Learning of Deep Networks using Model AveragingPreprint

McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 923 · see citing works

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 · see citing works

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 · see citing works

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

(2019) · doi:10.1109/infocom40694.2019 · Cited by 732 · see citing works

Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991401 · Cited by 712 · 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

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 · see citing works

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 · see citing works

A Proof of Security of Yao’s Protocol for Two-Party Computation

Lindell, Y., & Pinkas, B. (2008) · Journal of Cryptology · doi:10.1007/s00145-008-9036-8 · Cited by 654 · see citing works

Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

Bassily, R., Smith, A., & Thakurta, A. (2014) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2014.56 · Cited by 645 · see citing works

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 · see citing works

Federated Learning Over Wireless Fading Channels

Amiri, M. M., & Gunduz, D. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2974748 · Cited by 625 · see citing works

Reliable Federated Learning for Mobile Networks

Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900119 · Cited by 617 · see citing works

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

Ho, Q., Cipar, J., Cui, H., Kim, J. K., Lee, S., Gibbons, P. B., Gibson, G. A., Ganger, G. R., & Xing, E. P. (2013) · PubMed · doi:10.1184/r1/6475898 · Cited by 586 · see citing works

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Papernot, N., Abadi, M., Erlingsson, Ú., Goodfellow, I., & Talwar, K. (2016) · arXiv · doi:10.48550/arxiv.1610.05755 · Cited by 492 · see citing works

Privacy-Preserving Ridge Regression on Hundreds of Millions of Records

Nikolaenko, V., Weinsberg, U., Ioannidis, S., Jóye, M., Boneh, D., & Taft, N. (2013) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2013.30 · Cited by 447 · see citing works

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 · see citing works

Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks

Savazzi, S., Nicoli, M., & Rampa, V. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2964162 · Cited by 431 · see citing works

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 · see citing works

How the GDPR Will Change the World

Albrecht, J. P. (2016) · European Data Protection Law Review · doi:10.21552/edpl/2016/3/4 · Cited by 394 · see citing works

Petuum: A New Platform for Distributed Machine Learning on Big Data

Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., & Yu, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2472014 · Cited by 388 · see citing works

Improving Federated Learning Personalization via Model Agnostic Meta Learning

Jiang, Y., Konečný, J., Rush, K., & Kannan, S. (2019) · arXiv · doi:10.48550/arxiv.1909.12488 · Cited by 381 · see citing works

Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification

Du, W. K., Han, Y. S., & Chen, S. (2004) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.21 · Cited by 376 · see citing works

Think Locally, Act Globally: Federated Learning with Local and Global RepresentationsPreprint

Liang, P. P., Liu, T., Ziyin, L., Allen, N. B., Auerbach, R. P., Brent, D., Salakhutdinov, R., & Morency, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01523 · Cited by 375 · see citing works

Multi-center federated learning: clients clustering for better personalization

Long, G., Xie, M. X., Shen, T., Zhou, T., Wang, X., & Jiang, J. B. (2022) · World Wide Web · doi:10.1007/s11280-022-01046-x · Cited by 359 · see citing works

Three Approaches for Personalization with Applications to Federated LearningPreprint

Mansour, Y., Mohri, M., Ro, J. H., & Suresh, A. T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10619 · Cited by 337 · see citing works

A Crowdsourcing Framework for On-Device Federated Learning

Pandey, S. R., Tran, N. H., Bennis, M., Tun, Y. K., Manzoor, A., & Hong, C. S. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2971981 · Cited by 318 · see citing works

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 · see citing works

Transfer adaptation learning: A decade survey

Zhang, L., & Gao, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183326 · Cited by 314 · see citing works

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 · see citing works

IEEE International Conference on Communications

(2022) · IEEE Communications Magazine · doi:10.1109/mcom.2022.9711530 · Cited by 292 · see citing works

Privacy-preserving cooperative statistical analysis

Du, W. K., & Atallah, M. J. (2005) · Annual Computer Security Applications Conference · doi:10.1109/acsac.2001.991526 · Cited by 278 · see citing works

Coded Computation Over Heterogeneous Clusters

Reisizadeh, A., Prakash, S., Pedarsani, R., & Avestimehr, A. S. (2017) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2017.8006961 · Cited by 269 · see citing works

cpSGD: Communication-efficient and differentially-private distributed SGDPreprint

Agarwal, N., Suresh, A. T., Yu, F., Kumar, S., & McMahan, H. B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10559 · Cited by 264 · see citing works

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 261 · see citing works

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 · see citing works

Salvaging Federated Learning by Local AdaptationPreprint

Yu, T., Bagdasaryan, E., & Shmatikov, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04758 · Cited by 220 · see citing works

Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

Zeng, T., Semiari, O., Mozaffari, M., Chen, M., Saad, W., & Bennis, M. (2020) · ICC 2020 - 2020 IEEE International Conference on Communications (ICC) · doi:10.1109/icc40277.2020.9148776 · Cited by 218 · see citing works

Differentially Private Learning with Adaptive ClippingPreprint

Andrew, G., Thakkar, O. D., McMahan, H. B., & Ramaswamy, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03871 · Cited by 197 · see citing works

Perturbed Iterate Analysis for Asynchronous Stochastic Optimization

Mania, H., Pan, X., Papailiopoulos, D., Recht, B., Ramchandran, K., & Jordan, M. I. (2017) · SIAM Journal on Optimization · doi:10.1137/16m1057000 · Cited by 159 · see citing works

Robust Federated Learning With Noisy Communication

Ang, F., Chen, L., Zhao, N., Chen, Y., Wang, W., & Yu, F. R. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2979149 · Cited by 144 · see citing works

Privacy-Preserving News Recommendation Model Learning

Qi, T., Wu, F., Wu, C., Huang, Y., & Xie, X. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.128 · Cited by 141 · see citing works

Variational Federated Multi-Task LearningPreprint

Corinzia, L., Beuret, A., & Buhmann, J. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06268 · Cited by 118 · see citing works

A Survey on Differentially Private Machine Learning [Review Article]

Gong, M., Xie, Y., Pan, K., Feng, K., & Qin, A. K. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2020.2976185 · Cited by 115 · see citing works

Privacy Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products

Karr, A. F., Karr, A. F., Lin, X., Lin, X., Reiter, J. P., & Sanil, A. P. (2004) · Cited by 113 · see citing works

High-Performance Distributed ML at Scale through Parameter Server Consistency Models

Dai, W., Kumar, A., Wei, J., Ho, Q., Gibson, G. A., & Xing, E. P. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9195 · Cited by 109 · see citing works

InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy

Zhao, L., Ni, L., Hu, S., Chen, Y., Zhou, P., Xiao, F., & Wu, L. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2018.8486352 · Cited by 108 · see citing works

Managed communication and consistency for fast data-parallel iterative analytics

Wei, J., Dai, W., Qiao, A., Ho, Q., Cui, H., Ganger, G. R., Gibbons, P. B., Gibson, G. A., & Xing, E. P. (2015) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2806777.2806778 · Cited by 98 · see citing works

The impact of China's 2016 Cyber Security Law on foreign technology firms, and on China's big data and Smart City dreams

Parasol, M. (2017) · Computer law & security review · doi:10.1016/j.clsr.2017.05.022 · Cited by 96 · see citing works

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

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

Secure Linear Regression on Vertically Partitioned Datasets.Preprint

Gascón, A., Schoppmann, P., Balle, B., Raykova, M., Doerner, J., Zahur, S., & Evans, D. (2016) · IACR Cryptology ePrint Archive · Cited by 94 · see citing works

Privacy-preserving ridge regression on distributed data

Chen, Y.-R., Rezapour, A., & Tzeng, W.-G. (2018) · Information Sciences · doi:10.1016/j.ins.2018.03.061 · Cited by 88 · see citing works

International conference on communications ICC73

(1973) · Communications Society · doi:10.1109/mcomd.1973.1145811 · Cited by 87 · see citing works

Privacy-preservation for gradient descent methods

Wan, L. P., Ng, W. K., Han, S., & Lee, V. C. S. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281275 · Cited by 86 · see citing works

2013 IEEE 54th Annual Symposium on Foundations of Computer Science

(2013) · doi:10.1109/focs30697.2013 · Cited by 84 · see citing works

2008 Annual Computer Security Applications Conference (ACSAC)

(2008) · doi:10.1109/acsac14895.2008 · Cited by 83 · see citing works

Privacy-enhanced multi-party deep learning

Gong, M., Feng, J., & Xie, Y. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.10.001 · Cited by 78 · see citing works

Multi-View Fusion with Extreme Learning Machine for Clustering

Zhang, Y., Wu, J. X., Zhou, C., Cai, Z., Yang, J. Y., & Yu, P. S. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3340268 · Cited by 71 · see citing works

Federated Learning with Only Positive LabelsPreprint

Yu, F. X., Rawat, A. S., Menon, A. K., & Kumar, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10342 · Cited by 71 · see citing works

Semi-Cyclic Stochastic Gradient Descent

Eichner, H., Koren, T., McMahan, H. B., Srebro, N., & Talwar, K. (2019) · arXiv · doi:10.48550/arxiv.1904.10120 · Cited by 66 · see citing works

High-Performance Distributed ML at Scale through Parameter Server Consistency ModelsPreprint

Dai, W., Kumar, A., Wei, J., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.8043 · Cited by 65 · see citing works

Transfer Adaptation Learning: A Decade SurveyPreprint

Zhang, L., & Gao, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.04687 · Cited by 64 · see citing works

Estimation, Optimization, and Parallelism when Data is Sparse

Duchi, J. C., Jordan, M. I., & McMahan, H. B. (2013) · Cited by 63 · see citing works

Proceedings of the fourth SIAM International Conference on Data Mining

Berry, M. W. (2004) · Society for Industrial and Applied Mathematics eBooks · Cited by 46 · see citing works

General Principles of Civil Law of the People's Republic of China

Gray, W., & Zheng, H. R. (1986) · The American Journal of Comparative Law · doi:10.2307/840330 · Cited by 46 · see citing works

Conference on Computer and Communications Security - CCS 2011

Cortier, V., & Warinschi, B. (2011) · Cited by 40 · see citing works

Lightwave Power Transfer for Federated Learning-Based Wireless Networks

Tran, H.-V., Kaddoum, G., Elgala, H., et al. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.2985698 · Cited by 40 · see citing works

Secure collaborative few-shot learning

Xie, Y., Wang, H., Yu, B., & Zhang, C. B. C. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106157 · Cited by 40 · see citing works

DÏoT: A Crowdsourced Self-learning Approach for Detecting Compromised IoT DevicesPreprint

Nguyen, T. D., Marchal, S., Miettinen, M., et al. (2018) · arXiv (Cornell University) · Cited by 38 · see citing works

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

Kang, J., Xiong, Z., Niyato, D. T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07479 · Cited by 34 · see citing works

Customized Federated Learning for accelerated edge computing with heterogeneous task targets

Jiang, H., Liu, M., Yang, B., Liu, Q., Li, J., & Guo, X. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107569 · Cited by 34 · see citing works

A Privacy-Preserving Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

Zhang, C. B. C., Hu, X., Xie, Y., Gong, M., & Yu, B. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00112 · Cited by 31 · see citing works

From Discovery to Practice and Survivorship: Building a National Real‐World Data Learning Healthcare Framework for Military and Veteran Cancer Patients

Lee, J. S. H., Darcy, K. M., Hu, H., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1425 · Cited by 29 · see citing works

Adaptive Gradient-Based Meta-Learning MethodsPreprint

Khodak, M., Balcan, M.-F., & Talwalkar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02717 · Cited by 22 · see citing works

FedGroup: Ternary Cosine Similarity-based Clustered Federated Learning Framework toward High Accuracy in Heterogeneous Data.Preprint

Duan, M., Liu, D., Ji, X., et al. (2020) · arXiv (Cornell University) · Cited by 16 · see citing works

Theory and applications of models of computation (TAMC 2008)

Agrawal, M., & Li, A. (2010) · Theoretical Computer Science · doi:10.1016/j.tcs.2010.12.039 · Cited by 10 · see citing works

Intermittent Pulling with Local Compensation for Communication-Efficient Federated LearningPreprint

Wang, H., Qu, Z., Guo, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.08277 · Cited by 4 · see citing works

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

A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly

Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083 · see citing works

Federated learning on non-IID data: A survey

Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062 · see citing works

Federated learning for healthcare: Systematic review and architecture proposal

Antunes, R. S., André da Costa, C., Küderle, A., Yari, I. A., & Eskofier, B. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501813 · Cited by 595 · see citing works

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548 · see citing works

Federated learning for medical image analysis: A survey

Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518 · see citing works

Federated Learning for intrusion detection system: Concepts, challenges and future directions

Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.012 · Cited by 457 · see citing works

Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions

Habbal, A., Ali, M. K., & Abuzaraida, M. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122442 · Cited by 407 · see citing works

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 · see citing works

Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions

Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023) · ACM Computing Surveys · doi:10.1145/3570953 · Cited by 309 · see citing works

Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228 · see citing works

Privacy-Preserving Federated Learning Using Homomorphic Encryption

Park, J., & Lim, H. (2022) · Applied Sciences · doi:10.3390/app12020734 · Cited by 223 · see citing works

Applications of Federated Learning; Taxonomy, Challenges, and Research Trends

Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157 · see citing works

A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics

He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102963 · Cited by 147 · see citing works

Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey

Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152 · see citing works

Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey

Wu, J., Dong, F., Leung, H., Zhu, Z., Zhou, J., & Drew, S. (2024) · ACM Computing Surveys · doi:10.1145/3659205 · Cited by 130 · see citing works

Federated learning for healthcare applications

Chaddad, A., Wu, Y., & Desrosiers, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325822 · Cited by 136 · see citing works

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120 · see citing works

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114 · see citing works

Multimodal Deep Learning Approaches for Precision Oncology: A Comprehensive Review

Yang, H., Yang, M., Chen, J., Yao, G., Zou, Q., & Jia, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae699 · Cited by 115 · see citing works

A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data

Moulahi, W., Jdey, I., Moulahi, T., Alawida, M., & Alabdulatif, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107630 · Cited by 103 · see citing works

Machine learning in prostate MRI for prostate cancer: current status and future opportunities

Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H. (2022) · Diagnostics · doi:10.3390/diagnostics12020289 · Cited by 74 · see citing works

A survey for federated learning evaluations: Goals and measures

Chai, D., Wang, L., Yang, L., Zhang, J., Chen, K., & Yang, Q. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3382002 · Cited by 64 · see citing works

Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions

Lamaakal, I., Maleh, Y., El Makkaoui, K., Ouahbi, I., Pławiak, P., Alfarraj, O., Almousa, M., & Abd El-Latif, A. A. (2025) · Sensors · doi:10.3390/s25051318 · Cited by 60 · see citing works

Establishing and evaluating trustworthy AI: overview and research challenges

Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I. G., Šimić, I., et al. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1467222 · Cited by 59 · see citing works

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M. (2023) · Electronics · doi:10.3390/electronics12122703 · Cited by 51 · see citing works

Federated Learning For IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions

Adam, M., & Baroudi, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3506214 · Cited by 46 · see citing works

Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach

Bayram, F., & Ahmed, B. S. (2025) · ACM Computing Surveys · doi:10.1145/3708497 · Cited by 48 · see citing works

Label noise analysis meets adversarial training: A defense against label poisoning in federated learning

Hallaji, E., Razavi-Far, R., Saif, M., & Herrera-Viedma, E. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110384 · Cited by 41 · see citing works

Backdoor attacks-resilient aggregation based on Robust Filtering of Outliers in federated learning for image classification

Rodríguez-Barroso, N., Martínez-Cámara, E., Luzón, M. V., & Herrera, F. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108588 · Cited by 38 · see citing works

Defending local poisoning attacks in multi-party learning via immune system

Xie, F., Gao, Y., Wang, J., & Zhao, W. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107850 · Cited by 9 · see citing works

Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness

Nguyen-Le, H.-H., Tran, V.-T., Nguyen, D.-T., & Le-Khac, N.-A. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11608-4 · Cited by 1 · see citing works

CREATING MOBILE AND WEB FILTERED NEWS AGGREGATOR APPLICATION

Haryanto, R., & Martinez, J. J. L. (2020) · Journal of Critical Reviews · Cited by 1 · see citing works

Optimal participation of an electric vehicle aggregator in day-ahead energy and reserve markets

Sarker, M. R., Dvorkin, Y., & Ortega‐Vazquez, M. A. (2016) · IEEE Power and Energy Society General Meeting (PESGM) · doi:10.1109/pesgm.2016.7741311 · Cited by 18 · see citing works

Value of Aggregators

Akca, S., & Rao, A. (2020) · Marketing Science · doi:10.1287/mksc.2020.1236 · Cited by 13 · see citing works

An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits

Peng, C. H., Zou, J., Lian, L., et al. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.065 · Cited by 150 · see citing works

Comparison between Inflexible and Flexible Charging of Electric Vehicles—A Study from the Perspective of an Aggregator

Gomes, I. L. R., Melício, R., & Mendes, V. M. F. (2020) · Energies · doi:10.3390/en13205443 · Cited by 19 · see citing works