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 101–200 of 1,000
Rahman, A., Hasan, K., Kundu, D., Islam, M. J., Debnath, T., Band, S. S., & Kumar, N. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.08.004 · Cited by 115
Privacy-preserving blockchain-enabled federated learning for B5G-Driven edge computing
Wan, Y., Qu, Y., Gao, L., & Xiang, Y. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108671 · Cited by 114
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
Cao, X., Sun, G., Yu, H., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3172114 · Cited by 110
Nair, A. K., Sahoo, J., & Raj, E. D. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103720 · Cited by 120
Low-Latency Federated Learning Over Wireless Channels With Differential Privacy
Wei, K., Li, J., Ma, C., Ding, M., Chen, C., Jin, S., Han, Z., & Poor, H. V. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126052 · Cited by 109
Self-citationThe limits of differential privacy (and its misuse in data release and machine learning)
Domingo‐Ferrer, J., Sánchez, D., & Blanco-Justicia, A. (2021) · Communications of the ACM · doi:10.1145/3433638 · Cited by 109
Duan, Q., Hu, S., Deng, R., & Lu, Z. (2022) · Sensors · doi:10.3390/s22165983 · Cited by 109
Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks
Zhang, H., Zeng, K., & Lin, S. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3240291 · Cited by 114
Towards Fair Federated Learning with Zero-Shot Data Augmentation
Hao, W., El‐Khamy, M., Lee, J., Zhang, J., Liang, K. J., Chen, C., & Carin, L. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00369 · Cited by 113
FedIPR: Ownership Verification for Federated Deep Neural Network Models
Li, B., Fan, L., Gu, H., Li, J., & Yang, Q. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195956 · Cited by 115
Self-citationPrivacy and Fairness in Federated Learning: On the Perspective of Tradeoff
Chen, H., Zhu, T., Zhang, T., Zhou, W., & Yu, P. S. (2023) · ACM Computing Surveys · doi:10.1145/3606017 · Cited by 118
Privacy-Preserving Federated Learning With Malicious Clients and Honest-but-Curious Servers
Le, J., Zhang, D., Lei, X., Jiao, L., Zeng, K., & Liao, X. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3295949 · Cited by 110
RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity
Shen, J., Cheng, N., Wang, X., Lyu, F., Xu, W., Liu, Z., Aldubaikhy, K., & Shen, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3309633 · Cited by 117
Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning
Zhu, T., Zhou, W., Ye, D., Cheng, Z., & Li, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086910 · Cited by 109
Self-citationThapa, C., Chamikara, M. A. P., & Camtepe, S. A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_4 · Cited by 111
Black-Box Dataset Ownership Verification via Backdoor Watermarking
Li, Y., Zhu, M., Yang, X. S., Jiang, Y., Wei, T., & Xia, S. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3265535 · Cited by 111
Decentralized Wireless Federated Learning With Differential Privacy
Chen, S., Yu, D., Zou, Y., Yu, J., & Cheng, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3145010 · Cited by 100
A Platform-Free Proof of Federated Learning Consensus Mechanism for Sustainable Blockchains
Wang, Y., Peng, H., Su, Z., Luan, T. H., Benslimane, A., & Wu, Y. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3213347 · Cited by 105
A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis
Wang, T., Zhang, X., Feng, J., & Yang, X. (2020) · Sensors · doi:10.3390/s20247030 · Cited by 116
A Robust Game-Theoretical Federated Learning Framework With Joint Differential Privacy
Zhang, L., Zhu, T., Xiong, P., Zhou, W., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3140131 · Cited by 101
Efficient Verifiable Protocol for Privacy-Preserving Aggregation in Federated Learning
Eltaras, T., Sabry, F., Labda, W., Alzoubi, K., & AHMEDELTARAS, Q. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3273914 · Cited by 100
Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference Attacks
Zhang, S., Yuan, W., & Yin, H. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3295601 · Cited by 97
Fouda, M. M., Fadlullah, Z. M., Ibrahem, M. I., & Kato, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3486690 · Cited by 103
Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning
Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., Han, Z., & Vincent Poor, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3306773 · Cited by 96
Self-citationMali, S., Zeng, F., Adhikari, D., Ullah, I., Al‐Khasawneh, M. A., Alfarraj, O., & Alblehai, F. (2025) · Sensors · doi:10.3390/s25072197 · Cited by 96
Khosravy, M., Nakamura, K., Hirose, Y., Nitta, N., & Babaguchi, N. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3140687 · Cited by 94
Privacy-Preserving Federated Learning via Functional Encryption, Revisited
Chang, Y., Zhang, K., Gong, J., & Qian, H. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3255171 · Cited by 93
Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Survey
Vyas, A., Lin, P., Hwang, R., & Tripathi, M. (2024) · IEEE Access · doi:10.1109/access.2024.3454211 · Cited by 115
Protect Privacy from Gradient Leakage Attack in Federated Learning
Wang, J., Guo, S., Xie, X. N., & Qi, H. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796841 · Cited by 94
PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy
Wang, B., Chen, Y., Jiang, H., & Zhao, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3264259 · Cited by 92
Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions
Yaacoub, J.-P. A., Noura, H., & Salman, O. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.001 · Cited by 106
FedMultimodal: A Benchmark for Multimodal Federated Learning
Feng, T., Bose, D., Zhang, T., Hebbar, R., Ramakrishna, A., Gupta, R., Zhang, M., Avestimehr, S., & Narayanan, S. S. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599825 · Cited by 98
Batool, H., Anjum, A., Khan, A., Izzo, S., Mazzocca, C., & Jeon, G. (2024) · Information Sciences · doi:10.1016/j.ins.2023.119717 · Cited by 89
Privacy-preserving edge federated learning for intelligent mobile-health systems
Aminifar, A., Shokri, M., & Aminifar, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.035 · Cited by 106
Tu, L., Ouyang, X., Zhou, J., He, Y., & Xing, G. (2021) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3485730.3485946 · Cited by 88
Differentially Private Federated Learning With an Adaptive Noise Mechanism
Xue, R., Xue, K., Zhu, B., Luo, X., Zhang, T., Sun, Q., & Lü, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3318944 · Cited by 93
Federated Learning With Spiking Neural Networks
Venkatesha, Y., Kim, Y., Tassiulas, L., & Panda, P. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3121632 · Cited by 91
Trustworthy federated learning: privacy, security, and beyond
Chen, C., Liu, J., Tan, H., Li, X., Wang, K. I., Li, P., Sakurai, K., & Dou, D. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02285-2 · Cited by 110
Federated Edge Learning: Design Issues and Challenges
Taïk, A., & Cherkaoui, S. (2020) · IEEE Network · doi:10.1109/mnet.011.2000478 · Cited by 86
Pan, Y., Chao, Z., He, W., Yang, J., Li, H., & Liming, W. (2024) · Cybersecurity · doi:10.1186/s42400-024-00232-w · Cited by 99
A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges
Zhang, Y., Zeng, D., Luo, J., Fu, X., Chen, G., Xu, Z., & King, I. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3678181 · Cited by 99
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Xie, Y., Wang, Z., Gao, D., Chen, D., Yao, L., Kuang, W., Li, Y., Ding, B., & Zhou, J. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3579075.3579081 · Cited by 84
Privacy-preserving in Blockchain-based Federated Learning systems
Sameera, K. M., Nicolazzo, S., Arazzi, M., Nocera, A., A., R. R. K., Vinod, P., & Conti, M. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.04.024 · Cited by 107
AI on the edge: a comprehensive review
Su, W., Li, L., Liu, F., He, M., & Liang, X. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10141-4 · Cited by 90
A Differentially Private Federated Learning Model against Poisoning Attacks in Edge Computing
Zhou, J., Wu, N., Wang, Y., Gu, S., Cao, Z., Dong, X., & Choo, K. R. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3168556 · Cited by 93
Li, G., Wu, J., Li, S., Yang, W., & Li, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3173996 · Cited by 84
An IoT-Centric Data Protection Method for Preserving Security and Privacy in Cloud
Gupta, R., Gupta, I., Singh, A. K., Saxena, D., & Lee, C. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3218894 · Cited by 82
Server-Initiated Federated Unlearning to Eliminate Impacts of Low-Quality Data
Wang, P., Song, W., Qi, H., Zhou, C., Li, F., Wang, Y., Sun, P., & Zhang, Q. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3355188 · Cited by 90
Mohammadi, S., Balador, A., Sinaei, S., & Flammini, F. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104918 · Cited by 93
Wang, X., Wang, B., Wu, Y., Ning, Z., Guo, S., & Yu, F. R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3446585 · Cited by 101
Federated Learning and Its Role in the Privacy Preservation of IoT Devices
Alam, T., & Gupta, R. (2022) · Future Internet · doi:10.3390/fi14090246 · Cited by 84
IoT Malware Analysis Using Federated Learning: A Comprehensive Survey
Venkatasubramanian, M., Lashkari, A. H., & Hakak, S. (2023) · IEEE Access · doi:10.1109/access.2023.3235389 · Cited by 80
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
Yuan, X., Ni, W., Ding, M., Wei, K., Li, J., & Poor, H. V. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3258255 · Cited by 82
Self-citationQiao, C., Li, M., Liu, Y., & Tian, Z. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3399612 · Cited by 92
Saha, S., Hota, A., Chattopadhyay, A. K., Nag, A., & Nandi, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10766-7 · Cited by 94
Federated Learning in Robotic and Autonomous Systems
Xianjia, Y., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.041 · Cited by 79
A Compressive Sensing Approach for Federated Learning Over Massive MIMO Communication Systems
Jeon, Y., Amiri, M. M., Li, J., & Poor, H. V. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3038407 · Cited by 79
Self-citationA Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks
Ayaz, F., Sheng, Z., Tian, D., & Guan, Y. L. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3132226 · Cited by 79
ClusterFL: A Clustering-based Federated Learning System for Human Activity Recognition
Ouyang, X., Xie, Z., Zhou, J., Xing, G., & Huang, J. (2022) · ACM Transactions on Sensor Networks · doi:10.1145/3554980 · Cited by 79
Kourtellis, N., Katevas, K., & Perino, D. (2020) · doi:10.1145/3426745.3431337 · Cited by 79
FedRecAttack: Model Poisoning Attack to Federated Recommendation
Rong, D., Ye, S., Zhao, R., Yuen, H. N., Chen, J., & He, Q. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00243 · Cited by 78
A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
Xu, J., Kovatsch, M., Mattern, D., Mazza, F., Harasic, M., Paschke, A., & Lucia, S. (2022) · Applied Sciences · doi:10.3390/app12168239 · Cited by 95
Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
Tang, Q., Yu, F. R., Xie, R., Boukerche, A., Huang, T., & Liu, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3175453 · Cited by 81
3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning
Li, H., Ye, Q., Hu, H., Li, J., Wang, L., Fang, C., & Shi, J. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179401 · Cited by 84
Self-citationProjected federated averaging with heterogeneous differential privacy
Liu, J., Lou, J., Xiong, L., Liu, J., & Meng, X. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3503585.3503592 · Cited by 81
Privacy-Preserving and Low-Latency Federated Learning in Edge Computing
He, C., Liu, G., Guo, S., & Yang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3171767 · Cited by 77
Stephanie, V., Khalil, I., Atiquzzaman, M., & Yi, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3214998 · Cited by 82
Decentralized Collaborative Learning Framework for Next POI Recommendation
Long, J., Chen, T., Nguyen, Q. V. H., & Yin, H. (2022) · ACM Transactions on Information Systems · doi:10.1145/3555374 · Cited by 80
Enhancing Privacy-Preserving Intrusion Detection through Federated Learning
Alazab, A., Khraisat, A., Singh, S., & Jan, T. (2023) · Electronics · doi:10.3390/electronics12163382 · Cited by 75
How might technology rise to the challenge of data sharing in agri-food?
Durrant, A., Marković, M., Matthews, D., May, D., Leontidis, G., & Enright, J. A. (2021) · Global Food Security · doi:10.1016/j.gfs.2021.100493 · Cited by 74
Federated Learning-Based Collaborative Authentication Protocol for Shared Data in Social IoV
Zhao, P., Huang, Y., Gao, J., Xing, L., Wu, H., & Ma, H. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3153338 · Cited by 74
Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation
Deng, Y., Lyu, F., Ren, J., Chen, Y., Yang, P., Zhou, Y., & Zhang, Y. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3195207 · Cited by 79
Differential privacy in deep learning: A literature survey
Pan, K., Ong, Y.-S., Gong, M., Li, H., Qin, A. K., & Gao, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127663 · Cited by 89
On the privacy and security for e-health services in the metaverse: An overview
Letafati, M., & Otoum, S. (2023) · Ad Hoc Networks · doi:10.1016/j.adhoc.2023.103262 · Cited by 73
Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey
Rao, B., Zhang, J., Wu, D., Zhu, C., Sun, X., & Chen, B. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3363670 · Cited by 89
Interaction-level Membership Inference Attack Against Federated Recommender Systems
Yuan, W., Yang, C., Nguyen, Q. V. H., Cui, L., He, T., & Yin, H. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583359 · Cited by 90
Qu, Z., Tang, Y. S., Muhammad, G., & Tiwari, P. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101824 · Cited by 73
Performance-Enhanced Federated Learning With Differential Privacy for Internet of Things
Shen, X., Liu, Y., & Zhang, Z. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3189361 · Cited by 75
SecFed: A Secure and Efficient Federated Learning Based on Multi-Key Homomorphic Encryption
Cai, Y., Ding, W., Xiao, Y., Yan, Z., Liu, X., & Wan, Z. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3336977 · Cited by 76
Li, X., Zhao, H., Xu, J., Zhu, G., & Deng, W. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3479149 · Cited by 74
A robust analysis of adversarial attacks on federated learning environments
Nair, A. K., Raj, E. D., & Sahoo, J. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103723 · Cited by 70
Díaz, J. S.-P., & García, Á. L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.011 · Cited by 71
Federated Learning with Privacy-preserving and Model IP-right-protection
Yang, Q., Huang, A., Fan, L., Chan, C. S., Lim, J. H., Ng, K. W., Ong, D. S., & Li, B. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1343-2 · Cited by 88
VF 2 Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning
Fu, F., Shao, Y., Yu, L., Jiang, J., Xue, H., Tao, Y., & Cui, B. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457241 · Cited by 74
Secure Aggregation is Insecure: Category Inference Attack on Federated Learning
Gao, J., Hou, B., Guo, X., Liu, Z., Zhang, Y., Chen, K., & Li, J. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3128679 · Cited by 71
Self-citationA privacy-preserving model based on differential approach for sensitive data in cloud environment
Singh, A. K., & Gupta, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11751-w · Cited by 74
Yin, Z., Lin, Y., Zhang, Y., Qian, Y., Shu, F., & Li, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3188833 · Cited by 69
Self-citationDifferentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
Liu, X.-Y., Zhu, R., Zha, D., Gao, J., Zhong, S., White, M., & Qiu, M. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3682068 · Cited by 100
Safeguarding cross-silo federated learning with local differential privacy
Wang, C., Wu, X., Liu, G., Deng, T., Peng, K., & Wan, S. (2021) · Digital Communications and Networks · doi:10.1016/j.dcan.2021.11.006 · Cited by 67
FederatedTrust: A solution for trustworthy federated learning
Sánchez, P. M. S., Celdrán, A. H., Xie, N., Bovet, G., Pérez, G. M., & Stiller, B. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.10.013 · Cited by 82
Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review
Nawaz, A., Khan, S. S., & Ahmad, A. (2024) · IEEE Access · doi:10.1109/access.2024.3360691 · Cited by 83
Mengistu, T. M., Kim, T., & Lin, J.-W. (2024) · Sensors · doi:10.3390/s24030968 · Cited by 82
Fedface: Collaborative learning of face recognition model
Aggarwal, D., Zhou, J., & Jain, A. K. (2021) · 2021 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484386 · Cited by 67
Pan, Q., Wu, J., Bashir, A. K., Li, J., Yang, W., & Al‐Otaibi, Y. D. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3105492 · Cited by 68
Self-citationRanSAP: An open dataset of ransomware storage access patterns for training machine learning models
Hirano, M., Hodota, R., & Kobayashi, R. (2021) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2021.301314 · Cited by 81
Joint Privacy Enhancement and Quantization in Federated Learning
Lang, N., Sofer, E., Shaked, T., & Shlezinger, N. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3244092 · Cited by 69
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
Xu, R., Li, B. H., Li, C., Joshi, J. B. D., Ma, S., & Li, J. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3350206 · Cited by 75
Self-citationSecure Federated Learning by Power Control for Internet of Drones
Yao, J., & Ansari, N. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3076167 · Cited by 66
FedLess: Secure and Scalable Federated Learning Using Serverless Computing
Grafberger, A., Chadha, M., Jindal, A., Gu, J., & Gerndt, M. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9672067 · Cited by 74
The Internet of Federated Things (IoFT)
Kontar, R. A., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J. J., Kontar, W., Masoud, N., Nouiehed, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3127448 · Cited by 72
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
