The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Open accessAbstract
Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number$N$of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a$K$-client random scheduling strategy, where$K$($1\leq K< N$) clients are randomly selected from the$N$overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the$K$-client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal$K$that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Differentially private training algorithms62–64 allow for mitigation of information leakage from both the collaborator model updates and the global consensus aggregated models.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [63]
In the citing paper
“When it comes to FL, to prevent information leakage the concept of DPis utilized by adding noise to the parameters that are exchanged by the participants before model aggregation at the server side [25].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [25]
In the citing paper
“Three ML models are employed, including perceptron, support vector machine (SVM), and logistic regression, to perform FL training, showing that a competitive accuracy performance is achieved by using differential privacy-based FL methods compared to traditional FL schemes.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [82]
In the citing paper
“This can be done by using perturbation techniques [25] such as differential privacy can be used to protect training datasets against data breach, by constructing composition theorems with complex mathematical solutions.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [25]
In the citing paper
“Although some techniques have been used to overcome this problem, including differential privacy [166], these approaches degrade model performance or require additional conditions that are not suitable for IoT networks, especially high computing power [167].”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [166]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
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
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 Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
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
Introductory Lectures on Convex Optimization: A Basic Course
Нестеров, Ю. Е. (2014) · Medical Entomology and Zoology · Cited by 3,576
Cited by
Sorted by citations
Items 701–800 of 1,000
A Federated Learning Benchmark for Drug-Target Interaction
Mittone, G., Svoboda, F., Aldinucci, M., Lane, N. D., & Lió, P. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587687 · Cited by 15
Zhao, H. Q., Chen, H., Xiao, Y., & Zhang, Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095737 · Cited by 15
A security-friendly privacy-preserving solution for federated learning
Karakoç, F., Karaçay, L., Çomak, P., Gülen, U., Fuladi, R., & Soykan, E. Ü. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.05.004 · Cited by 17
Personalized Graph Federated Learning With Differential Privacy
Gauthier, F., Gogineni, V. C., Werner, S., Huang, Y.-F., & Kuh, A. (2023) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2023.3325963 · Cited by 16
Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning
Fu, W., Wang, H., Gao, C., Liu, G., Li, Y., & Jiang, T. (2023) · ACM Transactions on Information Systems · doi:10.1145/3633202 · Cited by 20
Xie, B., Hu, C., Huang, H., Yu, J., & Xia, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3340880 · Cited by 15
T-FedHA: A Trusted Hierarchical Asynchronous Federated Learning Framework for Internet of Things
Cao, Y., Liu, D., Zhang, S., Wu, T., Xue, F., & Tang, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123006 · Cited by 15
PVFL: Verifiable federated learning and prediction with privacy-preserving
Yin, B., Zhang, H., Lin, J., Kong, F., & Yu, L. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103700 · Cited by 18
Shiri, I., Salimi, Y., Sirjani, N., Razeghi, B., Bagherieh, S., Pakbin, M., Mansouri, Z., Hajianfar, G., Avval, A. H., Askari, D., et al. (2024) · Medical Physics · doi:10.1002/mp.16964 · Cited by 17
Mao, W., Ma, Q., Liao, G., & Chen, X. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3364372 · Cited by 16
Zhou, Z., Xu, C., Yang, S., Zhang, X., Li, H., Huang, S., & Muntean, G. (2024) · IEEE Network · doi:10.1109/mnet.2024.3371535 · Cited by 15
Blockchain and signcryption enabled asynchronous federated learning framework in fog computing
Zhou, Z., Tian, Y., Xiong, J., Peng, C., Li, J., & Yang, N. (2024) · Digital Communications and Networks · doi:10.1016/j.dcan.2024.03.004 · Cited by 15
Self-citationFaster Convergence on Differential Privacy-Based Federated Learning
Weng, S., Zhang, L., Zhang, X., & Imran, M. A. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3383226 · Cited by 19
A Survey on Privacy of Personal and Non-Personal Data in B5G/6G Networks
Sandeepa, C., Siniarski, B., Kourtellis, N., Wang, S., & Liyanage, M. (2024) · ACM Computing Surveys · doi:10.1145/3662179 · Cited by 18
Client-Side Gradient Inversion Attack in Federated Learning Using Secure Aggregation
Sun, Y., Liu, Z., Cui, J., Liu, J., Ma, K., & Liu, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3405939 · Cited by 18
SPFL: A Self-Purified Federated Learning Method Against Poisoning Attacks
Liu, Z., He, W., Chang, C.-H., Ye, J., Li, H., & Li, X. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3420135 · Cited by 15
Liberti, F., Berardi, D., & Martini, B. (2024) · Applied Sciences · doi:10.3390/app14188490 · Cited by 15
Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism
Wang, J., Zhang, Z., Tian, J., & Li, H. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110822 · Cited by 18
A hybrid and efficient Federated Learning for privacy preservation in IoT devices
Cao, S., Liu, S., Yang, Y., Du, W., Zhan, Z., Wang, D., & Zhang, W. (2025) · Ad Hoc Networks · doi:10.1016/j.adhoc.2025.103761 · Cited by 17
Exploiting Gaussian Noise Variance for Dynamic Differential Poisoning in Federated Learning
Hossain, M. T., Badsha, S., La, H. M., Islam, S., & Khalil, I. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3540030 · Cited by 15
Performance Optimization of Federated Learning over Mobile Wireless Networks
Chen, M., Poor, H. V., Saad, W., & Cui, S. R. (2020) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc48557.2020.9154300 · Cited by 15
Accuracy Degrading: Toward Participation-Fair Federated Learning
You, X., Liu, X., Lin, X., Cai, J., & Chen, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3238038 · Cited by 15
Lian, Z., Yang, Q., Zeng, Q., & Su, C. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838421 · Cited by 15
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical FrameworkPreprint
Ulhaq, A., & Burmeister, O. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06177 · Cited by 25
Harnessing wireless channels for scalable and privacy-preserving federated learning
Elgabli, A., Park, J., Issaid, C. B., & Bennis, M. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3078783 · Cited by 14
Okuno, M. J., & Okuno, H. G. (2025) · AI & Society · doi:10.1007/s00146-025-02288-9 · Cited by 18
FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption
Li, Y., Yan, N., Chen, J., Wang, X., Hong, J., He, K., Wang, W., & Li, B. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3567301 · Cited by 17
Atlas: A Framework for ML Lifecycle Provenance & Transparency
Spoczynski, M., Melara, M. S., & Szyller, S. (2025) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw67616.2025.00058 · Cited by 15
Differentially Private Real-Time Release of Sequential Data
Zhang, X., Khalili, M. M., & Liu, M. (2022) · ACM Transactions on Privacy and Security · doi:10.1145/3544837 · Cited by 14
Defending Byzantine attacks in ensemble federated learning: A reputation-based phishing approach
Li, B., Wang, P., Shao, Z., Liu, A., Jiang, Y., & Li, Y. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.05.002 · Cited by 15
Shahid, A. R., Imteaj, A., Badsha, S., & Hossain, M. Z. (2023) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp58114.2023.00085 · Cited by 14
AGC-DP: Differential Privacy with Adaptive Gaussian Clipping for Federated Learning
Hidayat, M. A., Nakamura, Y., Dawton, B., & Arakawa, Y. (2023) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm58254.2023.00042 · Cited by 16
Wang, Y., Guo, Q., & Yu, Y. (2023) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2023.3317012 · Cited by 14
A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks
Firdaus, M., Larasati, H. T., & Rhee, K.-H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039487 · Cited by 16
Federated Matrix Factorization Recommendation Based on Secret Sharing for Privacy Preserving
Zheng, X., Guan, M., Jia, X., Sun, L., & Luo, Y. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3322824 · Cited by 14
Federated Meta-Learning for Few-Shot Fault Diagnosis With Representation Encoding
Cui, J., Li, J., Mei, Z., Wei, K., Wei, S., Ding, M., Chen, W., & Guo, S. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3328074 · Cited by 15
Self-citationOn differential privacy for federated learning in wireless systems with multiple base stations
Tavangaran, N., Chen, M., Yang, Z., da Silva, J. M. B., & Poor, H. V. (2024) · IET Communications · doi:10.1049/cmu2.12722 · Cited by 14
Toward Few-Label Vertical Federated Learning
Zhang, L., Fu, L., Liu, C., Zhao, Y., Yang, J., Zheng, Z., & Chen, C. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3656344 · Cited by 14
Protecting Privacy in IoMT-based Disease Diagnosis Using an Efficient Approach
AbdelRaouf, H., Fouda, M. M., Fadlullah, Z. M., & Ibrahem, M. I. (2024) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi60790.2024.10586052 · Cited by 14
Secure and Efficient Collaborative Machine Learning Frameworks for 6G Intelligent Applications
Leong, W. Y. (2024) · IEEE International Workshop on Radio Frequency and Antenna Technologies (iWRF&AT) · doi:10.1109/iwrfat61200.2024.10594448 · Cited by 14
Towards Adaptive Privacy Protection for Interpretable Federated Learning
Li, Z., Chen, H., Ni, Z., Gao, Y., & Lou, W. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3443862 · Cited by 15
HierFedPDP:Hierarchical federated learning with personalized differential privacy
Li, S., Liu, Y., Feng, F., Liu, Y., Li, X., & Liu, Z. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103890 · Cited by 14
Ming, Y., Wang, S., Wang, C., Liu, H., Deng, Y., Zhao, Y., & Feng, J. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103279 · Cited by 17
FLEdge: Benchmarking Federated Learning Applications in Edge Computing Systems
Woisetschläger, H., Erben, A., Mayer, R., Wang, S., & Jacobsen, H. (2024) · International Middleware Conference · doi:10.1145/3652892.3700751 · Cited by 14
Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
Han, X., Chen, W., Li, J., Ding, M., Wu, Q., Wei, K., Deng, X., & Mei, Z. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3519355 · Cited by 14
Self-citationSecuring decentralized federated learning: cryptographic mechanisms for privacy and trust
Saidi, A. A., Amira, A., & Nouali, O. (2024) · Cluster Computing · doi:10.1007/s10586-024-04957-8 · Cited by 16
Hierarchical Federated Learning with Privacy
Chandrasekaran, V., Banerjee, S., Perino, D., & Kourtellis, N. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10826023 · Cited by 14
Zhou, J., Wu, J., Ni, J., Wang, Y., Pan, Y., & Su, Z. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3536612 · Cited by 15
Exploring the Privacy-Accuracy Trade-Off Using Adaptive Gradient Clipping in Federated Learning
Zhang, B., Mao, Y., He, X., Ping, P., Huang, H., & Wu, J. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3546777 · Cited by 14
On Defensive Neural Networks Against Inference Attack in Federated Learning
Lee, H.-K., Kim, J., Hussain, R., Cho, S., & Son, J. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500936 · Cited by 14
Utility Fairness for the Differentially Private Federated-Learning-Based Wireless IoT Networks
Alvi, S. A., Hong, Y., & Durrani, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3165596 · Cited by 17
Effect of Clustering in Federated Learning on Non-IID Electricity Consumption Prediction
Nightingale, J. S., Wang, Y., Zobiri, F., & Mustafa, M. (2022) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgt-europe54678.2022.9960569 · Cited by 14
Federated learning with superquantile aggregation for heterogeneous data
Pillutla, K., Laguel, Y., Malick, J., & Harchaoui, Z. (2023) · Machine Learning · doi:10.1007/s10994-023-06332-x · Cited by 18
Secure and Efficient Hierarchical Decentralized Learning for Internet of Vehicles
Liang, Z., Yang, P., Zhang, C., & Lyu, X. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3290625 · Cited by 14
RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything
Li, Z., Tian, Y., Zhang, W., Liao, Q., Liu, Y., Du, X., & Guizani, M. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3059958 · Cited by 14
Cai, H., Wang, J., Gao, L., & Li, F. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111511 · Cited by 13
Fed-GAN: Federated Generative Adversarial Network With Privacy-Preserving for Cross-Device Scenarios
Han, S., Ding, H., Zhao, S., Ren, S., Zeng, S., Xue, M., & Wang, R. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3567564 · Cited by 20
PriRoAgg: Achieving Robust Model Aggregation With Minimum Privacy Leakage for Federated Learning
Hou, S., Li, S., Jahani-Nezhad, T., & Caire, G. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3577498 · Cited by 14
Luo, X., Wang, A., Zhang, X., Huang, K., Wang, S., Chen, L., & Cui, Y. (2025) · Mathematics · doi:10.3390/math13213382 · Cited by 19
Deep federated learning: a systematic review of methods, applications, and challenges
Cooray, L., Sendanayake, J., Vithanaarachchi, P., & Priyadarshana, Y. H. P. P. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1617597 · Cited by 16
AspectFL: Aspect-Oriented Programming for Trustworthy and Compliant Federated Learning Systems
Alsobeh, A., Shatnawi, A., & Magableh, A. A. A. R. (2025) · Information · doi:10.3390/info16121048 · Cited by 16
Sawaika, A., Krishna, S., Tomar, T., Suggisetti, D. P., Lal, A., Shrivastav, T., Innan, N., & Shafique, M. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00175 · Cited by 17
Precision‐Weighted Federated Learning
Reyes, J., Jorio, L. D., Low‐Kam, C., & Kersten‐Oertel, M. (2025) · Computational Intelligence · doi:10.1111/coin.70150 · Cited by 29
Cluster-Based Secure Aggregation for Federated Learning
Kim, J., Park, G., Kim, M., & Park, S. (2023) · Electronics · doi:10.3390/electronics12040870 · Cited by 15
Federated learning and differential privacy in clinical health: Extensive survey
Odera, D. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0113 · Cited by 14
Gao, Y., Wang, P., Liu, L., Zhang, C., & Ma, H. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3591362 · Cited by 13
A Federated Learning-Based Framework for Ride-sourcing Traffic Demand Prediction
Hu, S., Ye, Y., Hu, Q., Liu, X., Cao, S., Yang, H. H., Shen, Y., Angeloudis, P., Parada, L., & Wu, C. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3287221 · Cited by 15
Self-citationA Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise
Jiao, S., Cai, L., Wang, X., Cheng, K., & Gao, X. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.030512 · Cited by 15
Dynamic behavior assessment protocol for secure Decentralized Federated Learning
Khan, S., Gomes, J., Rehman, M. H. U., & Svetinović, D. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100956 · Cited by 13
Privacy-Preserving Federated Class-Incremental Learning
Xiao, J., Tang, X., & Lu, S. (2023) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2023.3344074 · Cited by 13
Lu, S., Li, R., & Liu, W. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-023-2283-x · Cited by 13
Semi-supervised Federated Learning for Digital Twin 6G-enabled IIoT: A Bayesian estimated approach
Qi, Y., & Hossain, M. S. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.02.012 · Cited by 14
A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis
Tang, Y., Zhang, Y., Niu, T., Li, Z., Zhang, Z., Chen, H., & Zhang, L. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.030084 · Cited by 21
Arevalo, C. A., Noorbakhsh, S. L., Dong, Y., Hong, Y., & Wang, B. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28965 · Cited by 14
Dual-Server-Based Lightweight Privacy-Preserving Federated Learning
Zhong, L., Wang, L., Zhang, L., Domingo‐Ferrer, J., Xu, L., Wu, C., & Zhang, R. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3399534 · Cited by 14
Federated learning in smart home: A dynamic contract-based incentive approach with task preferences
Cao, M., Wang, Q., & Wang, Q. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110510 · Cited by 13
Cheng, Z., Liu, Y., Wu, C., Pan, Y., Zhao, L., Deng, X., & Zhu, C. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.035 · Cited by 13
Differential Privacy Federated Learning: A Comprehensive Review
Shan, F., Mao, S., Lu, Y., & Li, S. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150722 · Cited by 15
Mistry, D., Plabon, J. D., Diba, B. S., Mukta, S. H., & Mridha, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3448470 · Cited by 13
Federated quantum machine learning for drug discovery and healthcare
Saggi, M. K., Bhatia, A. S., & Kais, S. (2024) · Annual reports in computational chemistry · doi:10.1016/bs.arcc.2024.10.007 · Cited by 13
Usable Privacy and Security in Mobile Applications: Perception of Mobile End Users in Saudi Arabia
Saeed, S. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8110162 · Cited by 13
Machine Learning in Information and Communications Technology: A Survey
Dritsas, E., & Τρίγκα, Μ. (2024) · Information · doi:10.3390/info16010008 · Cited by 18
Optimal Rate Region for Key Efficient Hierarchical Secure Aggregation with User Collusion
Zhang, X., Wan, K., Sun, H., Wang, S., Ji, M., & Caire, G. (2024) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw61385.2024.10806947 · Cited by 14
RobustPFL: Robust Personalized Federated Learning
Chen, G., Wang, W., Wu, Y., Li, C., Xu, G., Ji, S., Li, T., Shen, M., & Han, Y. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3526840 · Cited by 18
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
Jiang, Y., Tong, X., Liu, Z., Ye, H., Tan, C. W., & Lam, K.-Y. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825236 · Cited by 13
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Chu, S., Li, J., Wang, J., Ni, Y., Wei, K., Chen, W., & Jin, S. (2025) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2025.3574143 · Cited by 14
Self-citationExamining Compliance with Personal Data Protection Regulations in Interorganizational Data Analysis
Li, S.-C., Chen, Y., & Huang, Y. (2021) · Sustainability · doi:10.3390/su132011459 · Cited by 13
In-Network Aggregation for Privacy-Preserving Federated Learning
Chen, F., Li, P., & Miyazaki, T. (2021) · International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) · doi:10.1109/ict-dm52643.2021.9664035 · Cited by 13
Majeed, A., Khan, S., & Hwang, S. O. (2022) · Electronics · doi:10.3390/electronics11091449 · Cited by 15
Zhou, Y., Liu, J., Wang, J. H., Wang, J., Li, C., Yu, S., Li, C., & Yu, S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.05.083 · Cited by 14
Edge Intelligence-based Privacy Protection Framework for IoT-based Smart Healthcare Systems
Akter, M., Moustafa, N., & Lynar, T. (2022) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798196 · Cited by 13
Joint Privacy Enhancement and Quantization in Federated Learning
Lang, N., & Shlezinger, N. (2022) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit50566.2022.9834551 · Cited by 13
Leveraging heuristic client selection for enhanced secure federated submodel learning
Liu, P., Zhou, T., Cai, Z., Liu, F., & Guo, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103211 · Cited by 13
Comment on “Federated Learning With Differential Privacy: Algorithms and Performance Analysis”
Rajkumar, K., Goswami, A., Lakshmanan, K. S., & Gupta, R. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3214717 · Cited by 13
Zhao, R., Yang, L. T., Liu, D., & Lu, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3283853 · Cited by 13
FedDebug: Systematic Debugging for Federated Learning Applications
Gill, W., Anwar, A., & Gulzar, M. A. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00053 · Cited by 13
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning
Goto, Y., Matsumoto, T., Rizk, H., Yanai, N., & Yamaguchi, H. (2023) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp58114.2023.00075 · Cited by 13
Differentially Private Coded Federated Linear Regression
Anand, A., Dhakal, S., Akdeniz, M. R., Edwards, B., & Himayat, N. (2021) · IEEE Data Science Workshop (DSW) · doi:10.1109/dslw51110.2021.9523408 · Cited by 13
A Secure and Privacy Preserving Federated Learning Approach for IoT Intrusion Detection System
Duy, P. T., Hao, H. N., Chu, H. M., & Pham, V.-H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92708-0_23 · Cited by 13
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration TimesPreprint
Fu, Y., Zhou, Y., Wu, D., Yu, S., Wen, Y., & Li, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04163 · Cited by 16
Metadata
- Authors
- Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H.
- Affiliations
- Nanjing University of Science and Technology (CN)
- Tomsk Polytechnic University (RU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Singapore University of Technology and Design (SG)
- Southeast University (CN)
- Department of Electrical Engineering, Princeton University, Princeton, NJ
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2020
- Volume
- 15
- Pages
- 3454-3469
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W3016632787
- Cited by
- 2,400
- References
- 40
- Citations by year
- 2026: 382 · 2025: 555 · 2024: 577 · 2023: 399 · 2022: 284 · 2021: 162 · 2020: 41
- FWCI
- 87.08
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2018YFB1004800)
- National Natural Science Foundation of China (61872184, 61727802)
- SUTD Growth Plan Grant for AI
- U.S. National Science Foundation (CCF-1908308)
- Princeton Center for Statistics and Machine Learning under a Data X Grant
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- federated learning, differential privacy, artificial noise, convergence bounds, privacy-utility trade-off, communication rounds, deep neural network training, information leakage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
