ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

Communication-Efficient Learning of Deep Networks from Decentralized Data

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Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754

Practical Secure Aggregation for Privacy-Preserving Machine Learning

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Privacy-preserving deep learning

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Federated Learning with Non-IID Data

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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

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Exploiting Unintended Feature Leakage in Collaborative Learning

Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734

On the Convergence of FedAvg on Non-IID Data

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How to backdoor federated learning

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Sylva: Tailoring Personalized Adversarial Defense in Pre-trained Models via Collaborative Fine-tuning

Qi, T., Xue, L., Zhan, Y., & Ma, X. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744805 · Cited by 2

PPFPL: Cross-Silo Privacy-Preserving Federated Prototype Learning Against Data Poisoning Attacks

Zhang, H., Yu, J., Xu, F., Hu, C., Zhang, Y., Wang, X., Yu, Z., & Zhang, X. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3643391 · Cited by 2

FRIDA: Free-rider detection using privacy attacks

Recasens, P. G., Horváth, Á., Gutierrez-Torre, A., Torres, J., Berral, J. L., & Pejó, B. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104357 · Cited by 3

Sign-Based Privacy-Preserving and Communication-Efficient Federated Learning in Large-Scale Edge Computing

Yang, C., Zhou, Z., Liang, Y., & Tang, C. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3665733 · Cited by 2

FedCST: Reliable federated learning under clustered data distribution in industrial internet of things

Tang, Y., Ni, L., Zhang, J., & Liang, Y. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112209 · Cited by 2

LSFL: A Lightweight and Secure Federated Learning scheme for Internet of Vehicles

Wei, L., Peng, D., Shi, Y., Shi, L., Li, G. Y., & Lian, H. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104858 · Cited by 2

Privacy-Preserving and Reliable Distributed Federated Learning

Dong, Y., Zhang, L., & Xu, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0834-5_9 · Cited by 2

Public Blockchain-Based Data Integrity Protection for Federated Learning in UAV Networks Using MAVLink Protocol

Khor, J. H., Sidorov, M., Law, S. Z., Tan, S. Y., & Woon, P. Y. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-9833-3_23 · Cited by 2

RADAR: Model Quality Assessment for Reputation-aware Collaborative Federated Learning

Lavaur, L., Lechevalier, P.-M., Busnel, Y., Ludinard, R., Pahl, M., & Texier, G. (2024) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds64841.2024.00030 · Cited by 7

Explore Patterns to Detect Sybil Attack during Federated Learning in Mobile Digital Twin Network

Zhang, W., Yu, C., Meng, Z., Shen, S., & Zhang, K. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622975 · Cited by 2

Lightweight Federated Learning Secure Aggregation Protocols

Mu, X., Tian, Y., Zhou, Z., & Xiong, J. (2024) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana63151.2024.00079 · Cited by 2

VSEPDA: Verifiable secure and efficient privacy-preserving data aggregation protocol for image classification in federated learning

Chen, S., Zhou, T., Xie, H., & Yang, X. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104039 · Cited by 2

DeFL-BC: Empowering Reliable Cyberattack Detection through Decentralized Federated Learning and Poisoning Attack Defense

Duy, P. T., Tran, T.-D., Duy, H. N., Hoang, H. D., & Pham, V.-H. (2023) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf60135.2023.10471775 · Cited by 2

Vulnerability detection under poisoning attacks through code and token featuresPreprint

González‐Manzano, L., & García-Alfaro, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4355876/v1 · Cited by 2

Federated Learning for Cross-Hospital Collaborative Research: A Comprehensive Survey of Applications, Challenges, and Future DirectionsPreprint

Liang, P., Chen, J., Yu, H., Huang, H., & Pu, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5383089 · Cited by 2

Patronus: Countering Model Poisoning Attacks in Edge Distributed DNN Training

Wu, Z., Xu, C., Wang, M., Ma, Y., Wu, Z., Xiahou, Z., & Grieco, L. A. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10571140 · Cited by 1

Progress and prospects of domain generalization mechanical fault diagnosis research

Shao, H., YiMing, X. I. A. O., Shen, Y. A. N., Jie, W. A. N. G., & Ying, P. E. N. G. (2024) · Scientia Sinica Technologica · doi:10.1360/sst-2024-0149 · Cited by 1

Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data

Zhang, J., Zhang, H., Wang, G., & Dong, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71467-2_6 · Cited by 1

Construction of a Deep Learning Model for Unmanned Aerial Vehicle-Assisted Safe Lightweight Industrial Quality Inspection in Complex Environments

Jing, Z., & Wang, R. (2024) · Drones · doi:10.3390/drones8120707 · Cited by 1

FedOPCS: An Optimized Poisoning Countermeasure for Non-IID Federated Learning with Privacy-Preserving Stability

Bai, F., Zhao, Y., Shen, T., Zeng, K., Zhang, X., & Zhang, C. (2025) · Symmetry · doi:10.3390/sym17050782 · Cited by 1

Cyber Security Concerns and Mitigation Strategies in Federated Learning: A Comprehensive Review

Ahir, P., & Parikh, M. C. (2025) · NFSU Journal of Cyber Security Digital Forensic · doi:10.69490/jcsdf920250526 · Cited by 1

False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions

Edelson, M., Pham, A., & Kuo, T.-T. (2025) · Scientific Reports · doi:10.1038/s41598-025-04069-2 · Cited by 1

Efficient and secure batch-aggregation scheme against poisoning attacks for federated learning in VANETs

Xie, Q., & Zhao, Y. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111493 · Cited by 2

General Test-Time Backdoor Detection in Split Neural Network-Based Vertical Federated Learning

Yuan, S., Li, X., Cao, X., Zhang, H., & Deng, R. H. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3595518 · Cited by 1

Deception-Based Defense of Poisoning Attack in Federated Learning

Masse, G. C. T., Benslimane, A., Tchendji, V. K., & Hemida, A. H. A. (2025) · IEEE Access · doi:10.1109/access.2025.3611806 · Cited by 1

A Truthful Incentive Scheme Based on Data Forgetting Game for Federated Unlearning

Xie, L., Su, Z., Wang, Y., Tao, J., & Lin, J. (2025) · IEEE International Conference on Privacy Computing and Data Security (PCDS) · doi:10.1109/pcds65695.2025.00030 · Cited by 2

Defense Against Poisoning Attacks on Federated Learning With Neighborhood Coulomb Force

Wang, B., Tian, Y., Guo, Y., & Li, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3637669 · Cited by 1

Deception-Based Defense Against Model Poisoning Attacks in Federated Learning Using Generative Adversarial Network (GAN)

Masse, G. C. T., Benslimane, A., Tchendji, V. K., Hemida, A. H. A., Su, Z., & Han, S. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161880 · Cited by 1

Lightweight orthogonal perturbation for privacy-preserving federated learning against poisoning attacks

Peng, C., & Xian, H. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104345 · Cited by 2

PDFL: A Privacy-Enhancing and Robust Poisoning Defense Federated Learning Scheme

Wang, G., Wu, H., Li, Q., Liu, Z., Zhao, J., Lam, K., Dong, Q., Yang, Y., & Zhou, L. J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3648147 · Cited by 1

FedD2: Data Poisoning Robust Defense Strategy in Federated Learning

Wu, J. F., Liu, Y., Ni, T., Zheng, X., Luo, Y., & Hu, P. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323206 · Cited by 1

SA-FL: Secure Aggregation Scheme in Federated Learning Against Poisoning Attacks

Chang, Y., Chen, Q., Luan, T. H., & Su, Z. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3656284 · Cited by 1

PEFL: A Privacy-Enhanced Federated Learning Framework for Mobile Edge CrowdSensing in the Presence of Collusion and Backdoor Attacks

Chen, M., Liu, X., Ma, W., Yu, K., Yu, J., Xie, J., & Sun, L. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3656307 · Cited by 1

A Robust Privacy-Preserving Federated Learning Method Against Poisoning Attacks on Non-IID Data

Fang, C., Chang, D., Li, Y., Yang, F., Liu, Y., & Ma, B. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3658384 · Cited by 1

Privacy-Preserving and Poisoning-Resilient Enhanced Cross-Silo Federated Learning

Zheng, X., Yan, X., Yang, F., Ng, W. W. Y., Li, Y., & Wu, S. (2025) · International Conference on Blockchain Technology and Information Security (ICBCTIS) · doi:10.1109/icbctis66509.2025.11387244 · Cited by 1

Game-Theoretic Defense Against Encrypted Data Poisoning in Federated Learning for Social Networks

Sun, Z., Gao, W., Yin, P., Li, W., Zhang, M., Chen, Y., & Shu, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7027-0_6 · Cited by 1

Adversarial Model Poisoning in CNNs for Brain Tumor Detection: Risks to Smart Healthcare Integrity

Singh, P., Lata, K., & Saini, S. (2025) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit66168.2025.11400258 · Cited by 1

TCFL: Trapdoor homomorphic encryption based federated learning aggregator with filter

Shen, X., Jia, Y., Xu, C. L., & Zhu, L. (2026) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02193-4 · Cited by 1

Toward Byzantine-Robust Federated Learning: Harmonizing Privacy and Efficiency via Locality-Sensitive Hashing

Jia, X., Sun, R. X., Liu, L., Liu, J., Kang, J., Mumtaz, S., & Dong, M. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3671265 · Cited by 1

FedUnit: A Robust and Fair Federated Learning Framework for Malicious Client Detection and Free-Rider Contribution Evaluation

Chen, L., Batool, H., Zeng, X., Zhao, D., Du, Y., & Tan, C. W. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114982 · Cited by 1

Trustworthy Distributed Load Forecasting in Resource‐Limited Smart Grids and Buildings via Random Layer Aggregation

Manzoor, H. U., Shabbir, A., Rais, R. N. B., Hussain, S., & Zoha, A. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/8810907 · Cited by 1

PRFed: Privacy-preserving federated learning to resist poisoning attack

Li, H., Chen, Y., Yang, Y., Dou, H., & Yang, Y. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104397 · Cited by 1

FLSeg: Enhancing Privacy and Robustness in Federated Learning under Heterogeneous Data via Model Segmentation

Su, Z., Lu, Z. G., Wu, Y. H., Shen, R., & Lu, S. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00373 · Cited by 1

Frequency-Domain Signatures for Proactive Defense Against Model Poisoning Attacks in Federated Learning

Hu, F., Zhang, A., Li, M., & Wang, C. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3689720 · Cited by 1

From data heterogeneity to convergence: A data-centric review of federated learning

Nguyen, H. T., Bettinelli, M., Ghaffari, A., Benoît, A., Nguyen, T., Pirttikangas, S., & Lovén, L. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135005 · Cited by 1

The shift from centralized analytics to federated learning in smart grids: a comprehensive review

Sultana, A., & Sarma, K. K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02793-3 · Cited by 1

A Byzantine-Robust Secure Federated Learning Scheme in Heterogeneous Data

Wang, R., Wang, R., Li, Z., Zhang, F., Li, J., & Li, X. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3714122 · Cited by 1

Privacy-Preserving and Models Intrusion Detection Federated Deep Learning Challenges, Schemas and Future Trajectories

Yu, Y., Jianping, L., & Weiwei, D. (2022) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip56608.2022.10016548 · Cited by 1

FedMC: Federated Learning with Mode Connectivity Against Distributed Backdoor Attacks

Wang, W., Zhang, C., Liu, S., Tang, M., Liu, A., & Yu, S. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278903 · Cited by 1

Integrating Differential Privacy and HE for Enhanced Privacy and Security in FL without Compromising Model Utility

Garg, P. (2023) · International Conference on Power Energy, Environment and Intelligent Control (PEEIC) · doi:10.1109/peeic59336.2023.10451626 · Cited by 1

An Adaptive Differential Private Federated Learning in Secure IoT

Xu, R., Li, Q., Cao, W., Yan, J., & Li, Y. (2023) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd63341.2023.00028 · Cited by 1

PROFL: A Privacy-Preserving Federated Learning Method with Stringent Defense Against Poisoning Attacks

Zhong, Y., & Wang, L.-P. (2024) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd61410.2024.10580526 · Cited by 1

R-PPDFL: A Robust and Privacy-Preserving Decentralized Federated Learning System

Chen, T., Wang, X., & Dai, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5101-3_9 · Cited by 1

Decentralized Federated Learning Links for Biometric Recognition

Guo, J., Mu, H., Ren, H., Han, C., & Sun, L. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650816 · Cited by 1

Privacy-Preserving and Robust Federated Learning Based on Secret Sharing

Mei, J., Shen, X., Xu, C. L., Zhu, L., Jin, G., & Sharif, K. (2024) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa63168.2024.00223 · Cited by 1

Byzantine-Robust Decentralized Federated Learning via Local Performance Checking

Zhang, K., Basharat, A., & Xu, P. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6954-7_12 · Cited by 1

Privacy-Preserving Federated Learning Compatible with Robust AggregatorsPreprint

Alebouyeh, Z., & Bidgoly, A. J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4793556 · Cited by 1

Secure Image Watermarking in Cloud Computing with Distributed Paillier Cryptosystem

Zhou, Z., Chen, B., Dong, C., Zheng, P., & Lu, W. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9949258

Privacy-preserving Federated Learning Against Byzantine Attack via Top-k Indexes

Shang, M., Tong, Z., Liu, T., & Zhang, T. (2023) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana60121.2023.00108

A Max-Min Security Game for Coordinated Backdoor Attacks on Federated Learning

Wahab, O. A., & Avila, A. R. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386756

PPBRFL: Privacy-Preserving Byzantine-Robust Federated Learning

Zhou, Q. (2024) · Frontiers in Computing and Intelligent Systems · doi:10.54097/6jamgy43

Privacy-Preserving Federated Learning Compatible with Robust AggregatorsPreprint

Alebouyeh, Z., & Bidgoly, A. J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4882102

Refine and Identify: An Accelerated Iterative Algorithm for Securing Federated Learning

Gouissem, A., Chkirbene, Z., Khattab, T. M., Mabrok, M. A., Abdallah, M. M., & Hamila, R. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592366

A Survey of Federated Learning: Review, Attacks, Defenses

Yao, Z., Cheng, J., Fu, C., & Huang, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-4387-2_13

Lightweight Byzantine-Robust and Privacy-Preserving Federated Learning

Lu, Z. G., Lu, S., Cui, Y., Wu, J., Nie, H., Xiao, J., & Yi, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-69766-1_19

Adversarial Node Placement in Decentralized Federated Learning: Maximum Spanning-Centrality Strategy and Performance Analysis

Piaseczny, A., Ruzomberka, E., Parasnis, R., & Brinton, C. G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3462674

Why Not Model Privacy?: An Efficient and Practical Scheme for Federated Learning Model Security

Wang, S., Bi, R., Tian, Y., & Xiong, J. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-60347-1_33

FEFL: A Fair & Efficient Defense Against Poisoning Attacks via Discrete Update Space

Zhao, Y., Zhang, J., Cao, Y., & Ye, T. (2024) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp62589.2024.10809244

A backdoor attack defense scheme in federated learning

Jing, Z., & He, J. (2024) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba62489.2024.10867968

An Asynchronous Robust Federated Learning Approach for Data Value in IoT

Liang, H., Jin, M., Yan, H., Han, S., Li, W., Yang, Z., & Wang, Z. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2468-3_20

Mitigating Iterative Fast Gradient Adversarial Poisoning in Archaeological Image Datasets using Generative Adversarial Networks and Denoising Autoencoders

Bai, K. V. S., Pradeep, K., Harini, G. V. N. L., & Reddy, N. Y. (2025) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears64219.2025.10940731

StegoFL: Using Steganography and Federated Learning to Transmit Malware

Rong, W., Liang, J., Jiang, H., Feng, C., & Chang, C. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00050

Federated Learning: An Overview of Attacks and Defense Methods

Sameera, K. M., Arikkat, D. R., Vinod, P., A., R. R. K., Aneez, A., & Conti, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-83157-7_14

LBRFL: Lightweight Privacy-Preserving Federated Learning with Byzantine-Robustness

Shen, P., Zhao, S., Xu, J., Zhao, C., Chen, Z., & Guo, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6606-5_3

Q-A DE2: A Novel Approach for Solving Federated Learning Incentive Mechanisms Based on Stackelberg Game

Guo, C., Zhao, S., & Qin, S. (2025) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec65147.2025.11043007

PARSIFAL: Private and Robust Sign Federated Learning

Lei, R., Wang, P., Zeng, J., Wang, C., Pei, H., & Zhao, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737074

Hypershield: A Personalized Hypernetwork-Based Defense Against Poisoning Attacks in Federated Learning for HealthcarePreprint

Dao, B. T., Duc, V. V., Vu, T. A., Nguyen, H. T., Tran, K. P., & Truong, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5362071

Privacy-preserving federated graph neural network against poisoning attack

He, G., Ren, Y., He, G., Feng, G., & Zhang, X. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.110214

AP-PPFL: an anti-poisoning privacy-preserving federated learning method

Li, Y., Chen, F., Chang, C., Guo, Y., Sun, H., & Hao, Y. (2026) · Cybersecurity · doi:10.1186/s42400-026-00583-6

BR-CPPFL: A Blockchain-Based Robust Clustered Privacy-Preserving Federated Learning System

Li, Y., Wang, X., Zhang, K., Zhang, B., Zheng, L., Ding, X., & Xu, Q. H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3543-9_20

Secure federated learning with configurable reliability in heterogeneous edge computing

Liu, Y., Liang, Y., Zhang, R., & Zhou, Z. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111808

A Defense Scheme of Backdoor Attacks for Federated Learning Based on Multi-Index Cascading

Guo, J., Xiong, Y., Wu, L., Dong, K., & Lee, L. (2025) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c65679.2025.00072

How to Defend Against Large-Scale Model Poisoning Attacks in Federated Learning: A Vertical Solution

Wang, J., Wang, R., & Zhang, F. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3630175

Enhancing Security and Privacy in Multi-Server Federated Learning

Zhao, X., Bu, Y., Fan, K., & Li, H. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3627244

From Heterogeneous Data to Secure Convergence: A Data-Centric Review of Federated LearningPreprint

Nguyen, H. Q., Bettinelli, M., Ghaffari, A., Benoît, A., Nguyen, T., Pirttikangas, S., & Lovén, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5856890

An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications

Mia, M. J., & Amini, M. H. (2025) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486115.044

Emerging Paradigms for Securing Federated Learning Systems

Abouelmagd, A. A., & Hilal, A. E. (2025) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot68269.2025.11275564

Sybil-aware adaptive defence framework for robust federated learning

Khedekar, D., Mahapatra, T., & Rajput, A. S. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102157

MFL-PAM: A Modular Federated Learning Framework for Privacy-Preserving and Adaptive Malware DetectionPreprint

Jbara, Y. H. F., Aloufi, F. A., & Alhalangy, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5933532

FBFL: Flexible Byzantine-Robust Federated Learning With Privacy-Preserving

Xu, Y., Zhang, H., Zhao, J., Gai, K., Tian, Z., & Yang, J. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3641757 · Cited by 1

Secure and Robust Federated Learning Under Dual-Server Architecture in Internet of Things

Liu, S., Ren, J., Meng, R., Yan, X., Miao, Y., Liu, Z., Peng, J., Zou, Y., Wang, Z., Li, H., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3647441

X-BDFL: Explainable Blockchain-Enabled Distributed Federated Learning Against Poisoning Attack in Healthcare

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FedDRLPD: Deep reinforcement Learning-Based defense mechanism against poisoning attacks in federated learning

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SplitGuard: A Robust Defense Framework for Split Federated Learning Against Poisoning Attacks via Spatiotemporal Trajectory Analysis

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FLET: Game-Theoretic Free-Riding Mitigation via Test Tasks in Federated Learning

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Privacy-Enhanced Federated Learning Against Poisoning Adversaries

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TPFL: Privacy-preserving personalized federated learning mitigates model poisoning attacks

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