Privacy-Enhanced Federated Learning Against Poisoning Adversaries
Abstract
Federated learning (FL), as a distributed machine learning setting, has received considerable attention in recent years. To alleviate privacy concerns, FL essentially promises that multiple parties jointly train the model by exchanging gradients rather than raw data. However, intrinsic privacy issue still exists in FL, e.g., user’s training samples could be revealed by solely inferring gradients. Moreover, the emerging poisoning attack also poses a crucial security threat to FL. In particular, due to the distributed nature of FL, malicious users may submit crafted gradients during the training process to undermine the integrity and availability of the model. Furthermore, there exists a contradiction in simultaneously addressing two issues, that is, privacy-preserving FL solutions are dedicated to ensuring gradients indistinguishability, whereas the defenses against poisoning attacks tend to remove outliers based on their similarity. To solve such a dilemma, in this paper, we aim to build a bridge between the two issues. Specifically, we present a privacy-enhanced FL (PEFL) framework that adopts homomorphic encryption as the underlying technology and provides the server with a channel to punish poisoners via the effective gradient data extraction of the logarithmic function. To the best of our knowledge, the PEFL is the first effort to efficiently detect the poisoning behaviors in FL under ciphertext. Detailed theoretical analyses illustrate the security and convergence properties of the scheme. Moreover, the experiments conducted on real-world datasets show that the PEFL can effectively defend against label-flipping and backdoor attacks, two representative poisoning attacks in FL.
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Homomorphic encryption can be used to evaluate the local model securely.
“To solve this problem, [38], [49], [51] used homomorphic encryption to evaluate the local model securely.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [51]
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TDFL: Truth Discovery Based Byzantine Robust Federated Learning
Xu, C. L., Jia, Y., Zhu, L., Zhang, C., Jin, G., & Sharif, K. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3205714 · Cited by 49 · see citing works
“According to prior work, the vanilla federated learning with the aggregation rule of average is vulnerable even if there is only one malicious participant [26].”
BackgroundEnhanced Security and Privacy via Fragmented Federated Learning
Jebreel, N., Domingo‐Ferrer, J., Blanco-Justicia, A., & Sánchez, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3212627 · Cited by 46 · see citing works
“PEFL [42] tries to address both privacy and security, but assumes there are two noncolluding servers that collaborate to filter out malicious updates while preventing each other from seeing individual updates.”
BackgroundRoseAgg: Robust Defense Against Targeted Collusion Attacks in Federated Learning
Yang, H., Xi, W., Shen, Y., Wu, C., & Zhao, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3352415 · Cited by 48 · see citing works
“Digital Object Identifier 10.1109/TIFS.2024.3352415 eliminate potentially poisoned model updates [1], [11], [17].”
BackgroundInfluentialVerifiable Federated Learning With Privacy-Preserving Data Aggregation for Consumer Electronics
Xie, H., Wang, Y., Ding, Y., Yang, C., Zheng, H., & Qin, B. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3323206 · Cited by 46 · see citing works
“As a technology that can ensure that the participants’ original data does not need to be uploaded to a central server, while avoiding centralized data collection and modeltraining [7], 1558-4127 c (cid:2) 2023 IEEE.”
BackgroundBlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework
Qin, Z., Yan, X., Zhou, M., & Deng, S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645425 · Cited by 46 · see citing works
“This problem is solved in several rounds, where in each round, some of the participants train w by a stochastic gradient descent (SGD) [27, 29] algorithm for several steps and then send the trained w (local model) to the server for aggregation.”
BackgroundMethodInfluentialSAFELearning: Secure Aggregation in Federated Learning With Backdoor Detectability
Zhang, Z., Li, J., Yu, S., & Makaya, C. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3280032 · Cited by 54 · see citing works
“In Table III, we compared our protocol with PEFL [27] (simulated via Paillier cryptosystem) in terms of client and server running time.”
BackgroundMethodToward Efficient and Certified Recovery From Poisoning Attacks in Federated Learning
Jiang, Y., Shen, J., Liu, Z., Tan, C. W., & Lam, K. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3533907 · Cited by 48 · see citing works
“…assess the direction similarity between a client’s update, i.e., gradients, and the global update, i.e., the aggregated gradients from all participating clients, in the same FL round, a widely employed metric for evaluating the similarity between two vectors in an FL setting [10], [52], [53], [54].”
Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning
Bondok, A. H., Abdallah, M. M., Badr, M. M., Fouda, M. M., Abdallah, M. M., & Alsabaan, M. (2023) · IEEE Access · doi:10.1109/access.2023.3323617 · Cited by 42 · see citing works
“Following the works in [38], [39], [40], and [41], We set an upper limit on the number of malicious consumers as follows : where M is the total number of malicious consumers and N is the total number of consumers where N ≥ 3.”
BackgroundMethodRPIFL: Reliable and Privacy-Preserving Federated Learning for the Internet of Things
Wang, R., Lai, J., Li, X., He, D., & Khan, M. K. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103768 · Cited by 42 · see citing works
Efficiently Achieving Privacy Preservation and Poisoning Attack Resistance in Federated Learning
Li, X.-Y., Yang, X. S., Zhou, Z., & Lu, R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3378006 · Cited by 40 · see citing works
“For example, [14] emphasizes that unprotected coefficients carry the risk of privacy leakage.”
BackgroundMethodInfluentialSelf-citationBPFL: Blockchain-based privacy-preserving federated learning against poisoning attack
Ren, Y., Hu, M., Yang, Z., Feng, G., & Zhang, X. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120377 · Cited by 34 · see citing works
Chen, L., Xiao, D., Xiao, X., & Zhang, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3486611 · Cited by 37 · see citing works
Tensor-Empowered Federated Learning for Cyber-Physical-Social Computing and Communication Systems
Yang, L., Zhao, R.-N., Liu, D.-B., Lu, W., & Deng, X. (2023) · IEEE Communications Surveys and Tutorials · doi:10.1109/comst.2023.3282264 · Cited by 22 · see citing works
“cess of clients are not visible to FL platform, it provides an inherent advantage for malicious clients to launch data poisoning attacks [13].”
BackgroundPapadopoulos, C., Kollias, K.-F., & Fragulis, G. F. (2024) · Future Internet · doi:10.3390/fi16110415 · Cited by 35 · see citing works
“In [123], the PEFL framework presented faces challenges in balancing privacy and security, with privacy-preserving methods hindering poisoning attack detection.”
ESVFL: Efficient and secure verifiable federated learning with privacy-preserving
Cai, J., Shen, W., & Qin, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102420 · Cited by 36 · see citing works
Fotohi, R., Aliee, F. S., & Farahani, B. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111764 · Cited by 35 · see citing works
“Li et al. (Li et al., 2021) proposed a method called FEDBN for cancer detection without sharing privacy-sensitive data.”
Wang, D., & Guan, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102796 · Cited by 33 · see citing works
Trustworthy Federated Learning Against Malicious Attacks in Web 3.0
Zheng, Y., Tian, Y., Zhou, Z., Li, T., Wang, S., & Xiong, J. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3350365 · Cited by 33 · see citing works
“Additionally, since users’ data in federated learning remains unshared, the server is often unable to discern whether a user has initiated a poisoning attack, which can result in a flawed or compromised aggregated model, affecting its accuracy [12], [13].”
BackgroundTowards robust and privacy-preserving federated learning in edge computing
Zhou, H., Zheng, Y., & Jia, X. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110321 · Cited by 32 · see citing works
A survey on privacy-preserving federated learning against poisoning attacks
Feng, X., & Cheng, W. (2024) · Cluster Computing · doi:10.1007/s10586-024-04629-7 · Cited by 33 · see citing works
“…privacy techniques to enable the comparison of metrics such as cosine similarity, Euclidean distance, etc. (1) Similarity-based approach with privacy protection Liu et al. proposed the first FL framework PEFL (Privacy-Enhanced Federated Learning) [50] for detecting abnormal data on ciphertext.”
Privacy preserving and secure robust federated learning: A survey
Han, Q., Lu, S., Wang, W., Qu, H., Li, J., & Gao, Y. (2024) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.8084 · Cited by 29 · see citing works
A Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks in Smart Grid
Li, X., Wen, M., He, S., Lu, R., & Wang, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3365142 · Cited by 29 · see citing works
“Liu et al. [18] used the correlation coefficient to evaluate the training quality of each participant but it required a trusted third party to generate secure parameters.”
BackgroundSelf-citationDPFLA: Defending Private Federated Learning Against Poisoning Attacks
Feng, X., Cheng, W., Cao, C., Wang, L., & Sheng, V. S. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3376255 · Cited by 29 · see citing works
“These similarity calculations operated on the encrypted gradi-entsusingHomomorphicEncryption(HE)[7],[8],[9],[10]arise expensivecomputationoverhead.”
MethodTrustiness-based hierarchical decentralized federated learning
Li, Y., Wang, X., Sun, R., Xie, X., Ying, S., & Ren, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110763 · Cited by 29 · see citing works
DefendFL: A Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks
Liu, J., Li, X., Liu, X., Zhang, H., Miao, Y., & Deng, R. H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3423397 · Cited by 27 · see citing works
“1) Privacy Protection: We use provable security based on simulation to prove that DefendFL achieves security, which are captured using the real-world versus ideal world game [30], [32].”
Self-citationSplit Aggregation: Lightweight Privacy-Preserving Federated Learning Resistant to Byzantine Attacks
Lu, Z. G., Lu, S., Cui, Y., Tang, X., & Wu, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3402993 · Cited by 27 · see citing works
“Here we choose GAS nested Dnc; b) Robust Privacy FL: PEFL [17], FFL [20] uses the angular distance and coordinate-dependent , removing gradients below a threshold.”
BackgroundMethodInfluentialDifferentially Private Set Intersection for Asymmetrical ID Alignment
He, Y., Tan, X., Ni, J., Yang, L. T., & Deng, X. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3207911 · Cited by 27 · see citing works
“W ITH ever-evolving concerns on privacy leakage, especially after the release of the General Data Protection Regulation [1], Federated Learning (FL) [2], [3], [4], [5] has been a heated topic, since it can analyze the patterns of”
BackgroundExplainable Data Poison Attacks on Human Emotion Evaluation Systems Based on EEG Signals
Zhang, Z., Umar, S., Hammadi, A. Y. A., Yoon, S.-Y., Damiani, E., Ardagna, C. A., Bena, N., & Yeun, C. Y. (2023) · IEEE Access · doi:10.1109/access.2023.3245813 · Cited by 25 · see citing works
“The most frequent method of producing this type of poisoning is through willful flipping of the data’s labels [16].”
MethodStealthy and Flexible Trojan in Deep Learning Framework
Wang, Y., Chen, K., Tan, Y., Huang, S., Ma, W., & Li, Y. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3164073 · Cited by 25 · see citing works
“Trojan attacks against various DNN models have attracted wide-spread attention [9], [10], [11], [12], [13], [14] and have become a serious threat to the security of the DNN supply chain.”
BackgroundLightweight and Dynamic Privacy-Preserving Federated Learning via Functional Encryption
Yu, B., Zhao, J., Zhang, K., Gong, J., & Qian, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3540312 · Cited by 25 · see citing works
“Liu et al. introduced PEFL, a PPFL framework designed to defend against poisoning attacks [37].”
SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework
Zhao, J., Zhu, H., Xu, W., Wang, F., Lu, R., & Li, H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3232955 · Cited by 23 · see citing works
“, Sybil attack [26], data poisoning attack [27], [28], etc.”
BackgroundSelf-citationEfficient Byzantine-Robust and Privacy-Preserving Federated Learning on Compressive Domain
Hu, G., Li, H., Fan, W., & Zhang, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3314748 · Cited by 25 · see citing works
“We analyze the functional advantages of our scheme by comparing with five state-of-the-art robust schemes, which are Krum [5], Trimmed Mean [6], AFA [7], RLR [11], PEFL [10], FLTrust [9], and FLOD [8].”
BackgroundMethodInfluentialSelf-citationLyu, X., Hou, X., Ren, C., Ge, X., Yang, P., Cui, Q., & Tao, X. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3374590 · Cited by 23 · see citing works
“Gradient indistin-guishability is the intrinsic feature of privacy-preserving FL solutions [42].”
BackgroundPRoT-FL: A privacy-preserving and robust Training Manager for Federated Learning
Gamiz, I., Regueiro, C., Jacob, E., Lage, Ó., & Higuero, M. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103929 · Cited by 23 · see citing works
FLAD: Byzantine-Robust Federated Learning Based on Gradient Feature Anomaly Detection
Tang, P., Zhu, X., Qiu, W., Huang, Z. R., Mu, Z., & Li, S. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3542437 · Cited by 23 · see citing works
“Given the significant distinctions between gradients from malicious and honest clients [5], server can construct anomaly detectors using server dataset to extract features from local gradients and categorizes them as honest or malicious.”
InfluentialA framework reforming personalized Internet of Things by federated meta-learning
You, L., Guo, Z., Yuen, C., Chen, C. Y., Zhang, Y., & Poor, H. V. (2025) · Nature Communications · doi:10.1038/s41467-025-59217-z · Cited by 23 · see citing works
“…Gradient Defence) methods 39 are used as baselines against the inverting gradient attack; and CMA (Coordinate-wise Median Aggregation) 40 , LFRPPFL (Label-Flipping-Robust and Privacy-Preserving FL) 41 and PEFL (Privacy-Enhanced FL) 42 are used as baselines against the local fl ipping attack.”
Comments on “Privacy-Enhanced Federated Learning Against Poisoning Adversaries”
Schneider, T. H., Suresh, A., & Yalame, H. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3238544 · Cited by 21 · see citing works
“However, using different pads for the coordinates does not address the issue since the use of the same pad is required for the SecPear algorithm to remain correct (cf. Proposition 1 in [7]).”
BackgroundMethodInfluentialVPPFL: A verifiable privacy-preserving federated learning scheme against poisoning attacks
Huang, Y., Yang, G., Zhou, H., Dai, H., Yuan, D., & Yu, S. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103562 · Cited by 21 · see citing works
Peng, J., Xing, H., Xu, L., Luo, S., Dai, P., Feng, L., Song, J. H., Zhao, B., & Xiao, Z. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3447087 · Cited by 20 · see citing works
“…instanceswithinthetrainingdata,therebyaidinginthedetection ofmaliciouslypoisonedsamples[29],[30],[31],[32],andfed-eratedlearning-baseddefensemethodsthatfilterparticipating members during aggregation or eliminate potentially malicious updates during the model aggregation stage [33], [34], [35], [36].”
BackgroundShalabi, E., Khedr, W. I., Rushdy, E., & Salah, A. (2025) · Information · doi:10.3390/info16030244 · Cited by 22 · see citing works
Building Trusted Federated Learning: Key Technologies and Challenges
Chen, D., Jiang, X., Zhong, H., & Cui, J. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12010013 · Cited by 20 · see citing works
“For example, the work in [15] designed an outlier data point detection mechanism that can effectively eliminate tag reversal and backdoor-based poisoning attacks.”
Privacy-Preserving Collaborative Learning: A Scheme Providing Heterogeneous Protection
Wang, X., Zhang, H., Yang, M., Wu, X., & Cheng, P. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3289546 · Cited by 20 · see citing works
“For instance, Liu et al. [14] introduced two noncollusive servers to aggregate the clients’ local models, where the interactive information among clients and servers is in ciphertext form.”
BackgroundCSRA: Robust Incentive Mechanism Design for Differentially Private Federated Learning
Yang, Y., Hu, M., Zhou, Y., Liu, X., & Wu, D. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3329441 · Cited by 20 · see citing works
“The results are consist with that in prior works [32], [33].”
ResultSelf-citationMBA: Backdoor Attacks Against 3D Mesh Classifier
Fan, L., He, F., Si, T., Fan, R., Ye, C., & Li, B. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3346644 · Cited by 20 · see citing works
“The first scenario [14], [21], [57], [58], [59], [60] is suitable for MGF-MBA .”
BackgroundWei, Z., Wang, J., Zhao, Z., & Shi, K. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107559 · Cited by 19 · see citing works
ROBY: A Byzantine-Robust and Privacy-Preserving Serverless Federated Learning Framework
Tang, X., Li, M., Shen, M., Kang, J., Zhu, L., Liu, Z., Yang, G., Niyato, D. T., & Deng, R. H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3589066 · Cited by 21 · see citing works
“A S PRIVACY concerns and data protection laws prevent organizations from openly sharing users’ private data [1], Federated Learning (FL) [2], [3], [4] has been proposed to enable multiple organizations (i.e., the data owners) to jointly train deep learning models without sharing private data.”
Efficient and Privacy-Preserving Federated Learning Against Poisoning Adversaries
Zhao, J., Zhu, H., Wang, F., Zheng, Y., Lu, R., & Li, H. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3377931 · Cited by 20 · see citing works
“To evaluate the effectiveness of ELFL, we compare its prediction accuracy with the vanilla FL scheme (Baseline) [1], a notable HE-based FL scheme (PEFL) [12], and a masking-based FL scheme with grouped aggregation (SBRDL) [19].”
BackgroundMethodSelf-citationQin, Z., Chen, F., Zhi, C., Yan, X., & Deng, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i13.29385 · Cited by 19 · see citing works
“…neither significantly alter the statistical characteristics of models as Gaussian-noise attacks (Blanchard et al. 2017) nor cause a distinct modification to the training data as label-flipping attacks (Liu et al. 2021), and thus, are more covert against many existing defenses (Zeng et al. 2022).”
BackgroundFVFL: A Flexible and Verifiable Privacy-Preserving Federated Learning Scheme
Wang, G., Zhou, L., Li, Q., Yan, X., Liu, X., & Wu, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3385479 · Cited by 20 · see citing works
“Research [10] shows that the method of poison attack makes the global model deviate, which reduces the accuracy of the model.”
MethodSelf-citationPEFL: Privacy-Preserved and Efficient Federated Learning With Blockchain
Tian, L., Lin, F., Gan, J., Jia, R., Zheng, Z., & Li, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3479328 · Cited by 19 · see citing works
“Privacy security is the core issue in FL and has attracted widespread research interest [15], [17], [18].”
Privacy-Preserving AI (Federated Learning) for Digital Forensics
Iyengar, S. S., Nabavirazavi, S., Hariprasad, Y., Prasad, H. B., & Mohan, C. K. (2025) · Signals and communication technology · doi:10.1007/978-3-031-89327-8_5 · Cited by 18 · see citing works
Evaluating Security and Robustness for Split Federated Learning Against Poisoning Attacks
Wu, X., Yuan, H., Li, X., Ni, J., & Lu, R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3490861 · Cited by 17 · see citing works
Self-citationSoomro, I. A., Rehman, H. U., Hussain, S. J., Iqbal, A., Khalid, W., & Yu, H. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3647642 · Cited by 19 · see citing works
“This limitation ensures a fair comparison with [42], [43], [17], [45], and [46], but also sets the stage for future research to extend our model to these more intricate threats, including various collusion scenarios and auditor threats.”
Privacy-Preserving and Poisoning-Defending Federated Learning in Fog Computing
Li, Y., Zhang, S., Chang, Y. Z., Xu, G., & Li, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3302795 · Cited by 17 · see citing works
“For addressing both poisoning attacks and privacy protection in FL, two recent works [10], [11] have been proposed, respectively, based on the technologies of DP [10] and 2PC-MPC [11].”
BackgroundMethodSelf-citationSF-CABD: Secure Byzantine fault tolerance federated learning on Non-IID data
Lin, X., Li, Y., Xie, X., Ding, Y., Wu, X., & Ge, C. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111851 · Cited by 17 · see citing works
Gautam, V., Kaur, G., Malik, M., Pawar, A., Singh, A., Singh, K. K., Askar, S., & Abouhawwash, M. (2024) · IEEE Access · doi:10.1109/access.2024.3523257 · Cited by 17 · see citing works
“An algorithm was proposed in [32] to address the issue of data heterogeneity of data spread across the network.”
FlexibleFL: Mitigating poisoning attacks with contributions in cloud-edge federated learning systems
Zhao, Y., Cao, Y., Zhang, J., Huang, H., & Liu, Y. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120350 · Cited by 16 · see citing works
Privacy-preserving federated learning compatible with robust aggregators
Alebouyeh, Z., & Bidgoly, A. J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110078 · Cited by 16 · see citing works
BPS-FL: Blockchain-Based Privacy-Preserving and Secure Federated Learning
Yu, J., Yao, H., Ouyang, K., Cao, X., & Zhang, L. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020053 · Cited by 15 · see citing works
A Novel Blockchain-Assisted Aggregation Scheme for Federated Learning in IoT Networks
Liu, Z., Zheng, K., Hou, L., Yang, H., & Yang, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3277463 · Cited by 15 · see citing works
“[34] proposed a privacy-enhanced FL framework based on homomorphic encryption, but the framework increased the load on the module that deployed the global model.”
BackgroundZeng, P., Liu, A., Xiong, N. N., Zhang, S., & Dong, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3298814 · Cited by 15 · see citing works
“The other is to identify the reliability of IEDs when employing IEDs and select the trusted IEDs training model to obtain the trusted global model [3], [4].”
BackgroundMethodInfluentialPBFL: Privacy-Preserving and Byzantine-Robust Federated-Learning-Empowered Industry 4.0
Li, W., Fan, K., Yang, K., Yang, Y., & Li, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3315226 · Cited by 15 · see citing works
“Therefore, some works [13], [27], [28], [29] further ensure local data confidentiality with 2PC techniques.”
BackgroundMethodResultInfluentialSelf-citationElZemity, A., & Arief, B. (2024) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics62450.2024.00072 · Cited by 16 · see citing works
- Authors
- Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., & Lu, R.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Nanyang Technological University (SG)
- China Electronics Technology Group Corporation (CN)
- University of New Brunswick (CA)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2021
- Volume
- 16
- Pages
- 4574-4588
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W3198262235
- Cited by
- 351
- References
- 47
- Citations by year
- 2026: 47 · 2025: 102 · 2024: 110 · 2023: 66 · 2022: 23 · 2021: 3
- FWCI
- 17.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- National Natural Science Foundation of China (62020106013, 61972454, 61802051, 61772121, 61728102)
- Sichuan Science and Technology Program (2020JDTD0007, 2020YFG0298)
- Chongqing Science and Technology Commission (cstc2018jcyjAX0703)
- Fundamental Research Funds for Chinese Central Universities (ZYGX2020ZB027)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- privacy-preserving federated learning, poisoning attacks, homomorphic encryption, gradient privacy, poisoning detection, label-flipping attacks, backdoor attacks
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