Threats to Federated Learning

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

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772

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

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

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961

Deep Leakage from Gradients

Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906

Accelerating Federated Learning via Sequential Training of Grouped Heterogeneous Clients

Silvi, A., Rizzardi, A., Caldarola, D., Barbara Caputo, B., & Ciccone, M. (2024) · IEEE Access · doi:10.1109/access.2024.3387453 · Cited by 2

Risk Assessment in AI System Engineering: Experiences and Lessons Learned from a Practitioner’s Perspective

Fuchs, M., Fischer, L., Montuoro, A., Kumar, M., & Moser, B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-68302-2_6 · Cited by 2

Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning

Assumpção, N. R. G., & Villas, L. A. (2024) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc61673.2024.10733713 · Cited by 2

Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

Shen, X., Li, H., Li, Y., Zhang, W., Alenazi, M. J. F., & Agarwal, K. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3510726 · Cited by 2

Fed-Tra: Improving Accuracy of Deep Learning Model on Non-iid in Federated Learning

Xiao, W., Tang, X., Zhou, B., Wang, W., Dong, Y., Zang, L., Han, J., & Hu, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95384-3_49 · Cited by 2

Integration of online deep reinforcement learning and federated learning: A dynamic energy management strategy for industrial applications

Tang, Y., Du, G., Long, J., & Zhao, Y. (2023) · IET Communications · doi:10.1049/cmu2.12686 · Cited by 2

Federated Learning based Gender Classification in Heterogeneous and Distributed Data having Concept Drift

Sharma, V., Challa, V. K. R., Pranavi, P., & Singh, R. P. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2024.12.033 · Cited by 2

Defending against diverse attacks in federated learning through consensus-based bi-level optimization

Trillos, N. G., Akash, A. K., Li, S., Riedl, K., & Zhu, Y. (2025) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0235 · Cited by 2

Fostering Trustworthiness of Federated Learning Ecosystem through Realistic Scenarios

Psaltis, A., Zafeirouli, K., Leškovský, P., Bourou, S., Vásquez-Correa, J. C., Pablos, A. G., Sanchez, S. C., Dimou, A., Patrikakis, C. Z., & Daras, P. (2023) · Information · doi:10.3390/info14060342 · Cited by 2

An Approach of Federated Learning in Artificial Intelligence for Healthcare Analysis

Govindwar, G. D., & Dhande, S. S. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-3485-0_8 · 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

Multi–Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks

Ponomarenko-Timofeev, A., Galinina, O., Balakrishnan, R., Himayat, N., Andreev, S. D., & Koucheryavy, Y. A. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3546550 · Cited by 2

Leveraging the Domain Experts: Specializing Privacy Threat Knowledge

Sion, L., Landuyt, D. V., & Joosen, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82349-7_33 · Cited by 2

Simeon -- Secure Federated Machine Learning Through Iterative FilteringPreprint

Malecki, N., Paik, H.-Y., Ignjatović, A., Blair, A., & Bertino, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07704 · Cited by 2

Privacy-Preserving Federated Learning for Healthcare: A Synergistic Approach using Differential Privacy and Homomorphic EncryptionPreprint

Babu, K. M., Bhavitha, E., Mythri, M., Anusha, A., Chandana, B. S., & Akhtar, C. G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5088943 · Cited by 2

Analysis and Prediction of Outdoor Human Mobility Using Collaborative Learning

Marin, I.-V., Ciurezu, E.-C., Ciobanu, R., & Dobre, C. (2024) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot61029.2024.00088 · Cited by 1

SparseHE: An Efficient Privacy-Preserving Biomedical Prediction Approach Using Sparse Homomorphic Encryption

Song, C., Zhan, W., & Shi, X. M. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00021 · Cited by 1

I. Federated Learning: Pushing Forward Machine Learning Without Scarifising Privacy

Ghade, O., & Narad, S. (2024) · International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) · doi:10.1109/icaiqsa64000.2024.10882458 · Cited by 1

Privacy-Preserving Federated Learning in Oil Palm Industry for Fresh Fruit Bunch Ripeness Grading

Laddawong, P., Pianroj, Y., Chotikawanid, P., Punvichai, T., Jumrat, S., & Muangprathub, J. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.3.434-446 · 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

Federated Learning for Intrusion Detection Systems: Taxonomy, Threats, and Challenges

Mohammed, H. A., & Ali, A. K. (2025) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258141 · Cited by 1

Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches

Sokhankhosh, A., Hassan, K., & Rouhani, S. (2025) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain67634.2025.00033 · Cited by 1

Data-Aware Clustered Federated Learning in WSNs for Natural Disaster Management

Belfeki, Z., Krichen, M., Bouazizi, M., & Zidi, S. (2025) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa66935.2025.11315484 · Cited by 1

QFedPhish: A Quantum-Inspired Federated Learning Approach for Detecting AI-Generated Phishing Attacks

Malik, A., & Qaisar, S. M. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3652563 · Cited by 1

Backdoor Detection in Federated Learning With Feature Map: A Multi-Task Learning Perspective

Sui, Y., Sun, Y., Chen, N., Zhao, Y., Cao, H., & Xu, B. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3655920 · Cited by 1

A Secure and Privacy-Preserving Paradism Based on Blockchain and Federated Learning for CIoMT in Smart Healthcare Systems

Haddouti, S. E., & Kettani, M. D. E.-C. E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-53824-7_41 · Cited by 1

Byzantine Robust Aggregation in Federated Distillation with Adversaries

Li, W., Gu, H., Wan, S., Luan, Z., Xi, W., Fan, L., Yang, Q., & Chen, B. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00086 · Cited by 1

Secure Coalition Formation for Federated Machine Learning

Thakur, S., & Breslin, J. G. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-66694-0_15 · Cited by 1

A systematic review of the foundations, applications, and challenges of federated learning

Fateh, H., Rezvani, M., & Tahanian, E. (2024) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.21.3.23 · Cited by 1

Integrating CP-ABE and Device Fingerprint Into Federated Learning

Chen, C., Román, R., Wang, K. I., & Sakurai, K. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-2417-1_23 · Cited by 1

Privacy Leakage in Federated Learning for Smart Grid Short-Term Load Forecasting

Aly, H., Al-Ali, A. K., Al-Ali, A. K., & Malluhi, Q. M. (2024) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon55916.2024.10905338 · Cited by 1

Mdsd: a multi-dimensional scaling-based defensive mechanism against backdoor attacks on federated learning

Chen, Q., & Tao, Y. (2025) · Cluster Computing · doi:10.1007/s10586-024-04974-7 · Cited by 1

Multi-stage federated learning with group-wise bidirectional guidance

Li, X., Bian, T., Yang, J., Meng, D., Lu, Y., Jiang, X., & Liang, H. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07449-7 · Cited by 1

HoneyFL: Using Honeypots to Catch Backdoors in Federated Learning

Zheng, H., Shen, W., & Chen, J. (2025) · IET Image Processing · doi:10.1049/ipr2.70201 · Cited by 1

The Role of Federated Learning in AI-Powered Integrated Healthcare Solutions

Ahmed, K., Baranwal, A., Sharma, N., Garg, P., & Singh, N. S. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3306-9.ch015 · Cited by 1

Cerflan: Communication-Efficient norm bounding for robust federated learning

Li, S., Wei, X., Wang, H., Su, Y., & Susilo, W. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103909 · Cited by 1

Software Engineering Aspects of Federated Learning Libraries: A Comparative Survey

Alsghaier, H., & Zhao, T. (2025) · Software · doi:10.3390/software4040028 · Cited by 1

SoK: Towards Privacy-Centric Collaborative Machine Learning—A Classification Framework for Privacy Solutions

Boar, P. E., Sharma, P. K., Akram, R. N., & Li, W. (2025) · SN Computer Science · doi:10.1007/s42979-025-04482-4 · Cited by 1

Adversarially resilient federated learning for heterogeneous edge nodes in 5G networks with non-IID data

Zafar, S., Legg, P., White, J., & Salman, A. (2026) · Internet of Things · doi:10.1016/j.iot.2026.101919 · Cited by 1

Privacy-Preserving Distributed Support Vector Machines

Bottoni, S., Braghin, S., Brisimi, T. S., & Trombetta, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93663-1_8 · Cited by 1

Development of a Prototype for a Process Support and Analysis Platform for Small and Medium-sized Enterprises

Kallisch, J., & Wunck, C. (2022) · EPiC series in computing · doi:10.29007/jmrk · Cited by 1

Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization

Schroth, C. A., Vlaski, S., & Zoubir, A. M. (2023) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/dsp58604.2023.10167919 · Cited by 1

Preventing Text Data Poisoning Attacks in Federated Machine Learning by an Encrypted Verification Key

Jodayree, M., He, W., & Janicki, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-50959-9_42 · Cited by 1

Empowering Federated Learning: Tackling Client Drift and Resource Constraints with Knowledge Distillation by Combining Losses

Khan, M., Glavin, F. G., & Nickles, M. (2023) · Irish Conference on Artificial Intelligence and Cognitive Science (AICS) · doi:10.1109/aics60730.2023.10470505 · Cited by 1

Rápido, Privado e Protegido: Uma Abordagem para Aprendizado Federado Eficiente em Ambiente Hostil

Assumpção, N. R. G., & Villas, L. A. (2024) · doi:10.5753/courb.2024.2523 · Cited by 1

The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks

Piaseczny, A., Ruzomberka, E., Parasnis, R., & Brinton, C. G. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622678 · Cited by 1

PraaS: Verifiable Proofs of Property as-a-Service with Intel SGX

Akkuş, İ. E., Rimac, I., & Chen, R. (2024) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw61312.2024.00027 · Cited by 1

Blockchain-Based Personalized Federated Learning Leveraging the SMPC protocol

Dhumale, S., Ameria, D., & Shukla, S. K. (2023) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/iccst59048.2023.10726855 · Cited by 1

Stake-Driven Rewards and Log-Based Free Rider Detection in Federated Learning

Nguyen, H. M., Nguyen, T., Lovén, L., & Pirttikangas, S. (2024) · Conference on Privacy, Security and Trust · doi:10.1109/pst62714.2024.10788055 · Cited by 1

Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition

Tan, C.-L., Li, S., Cao, Y., Ren, Z. X., & Schultz, T. (2024) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3696409.3700187 · Cited by 1

Overcoming Trust and Incentive Barriers in Decentralized AI

Singh, A., Lu, C., & Raskar, R. (2024) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi62246.2024.00021 · Cited by 1

Decentralized federated learning based on committees and blockchain

Wang, Y., Yan, Y., Yang, C., Yang, T., She, Z., & Feng, H. (2026) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2026.105312 · Cited by 1

Robust and privacy preserving distributed machine learning

Talbi, R. (2021) · theses.fr (ABES) · doi:10.70675/c39e4a00z7d9dz4d18za65ez8d2292e27605 · Cited by 1

Robust and Privacy-Preserving Federated Learning

Elhattab, F. (2023) · theses.fr (ABES) · doi:10.70675/062161c5z9cdez4465z92bezb97b4a23cb36 · Cited by 2

Mechanism and Recent Trends of Federated Learning as a Privacy Technology

Seito, T. (2022) · IEICE ESS FUNDAMENTALS REVIEW · doi:10.1587/essfr.16.3_196

A Voting-Based Poisoning Attack to Federated Learning with Quantization

Ma, X., Wei, L., Zhang, B., Wang, Y., Zhang, C., & Li, Y. (2022) · International Conference on Hot Information-Centric Networking (HotICN) · doi:10.1109/hoticn57539.2022.10036222

GAN Attacks and Counterattacks in Federated Learning

Yu, S., & Cui, L. (2022) · Digital Privacy and Security · doi:10.1007/978-981-19-8692-5_4

DISBELIEVE: Distance Between Client Models Is Very Essential for Effective Local Model Poisoning Attacks

Joshi, I., Upadhya, P., Nayak, G. K., Schüffler, P. J., & Navab, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47401-9_29

FedCMK: An Efficient Privacy-Preserving Federated Learning Framework

Lu, P., Meng, X., & Liu, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-99-9785-5_18

A Comprehensive Analysis of Inference Attacks in Federated Learning

Ameria, D., Kumar, U., & Sankhe, S. R. (2023) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon58714.2023.10450123 · Cited by 1

Enhancing communication efficiency in federated machine learning: Current trends and future directions

Goel, P., Rana, I., & Sharma, B. (2024) · AIP conference proceedings · doi:10.1063/5.0198645

Data Poisoning Attack Based on Privacy Reasoning and Countermeasure in Federated Learning

Lv, J., Xu, S., Ling, Y., Man, D., Han, S., & Yang, W. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00074

ABD-HFL: Byzantine-resistant Decentralized Hierarchical Federated Learning

An, T., Serge Fdida, B., Potop-Butucaru, M., & Tixeuil, S. (2025) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/3694906.3743352 · Cited by 1

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

A Maneuver in the Trade-Off Space of Federated Learning Aggregation Frameworks Secured with Polymorphic Encryption: PolyFLAM and PolyFLAP Frameworks

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2024) · Electronics · doi:10.3390/electronics13183716 · Cited by 1

Timeliness-Selective Incentive Federated Crowdsourcing

Jiang, X., Diao, H., Zhou, C., & Zhang, J. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00083

Secure Federated Distillation Framework for Encrypted Traffic Classification

Teng, L., Feng, Q. B., Zhao, W., Luo, M., & He, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9053-1_1

CLB-BAFL: Critical Learning Behaviour Verification Mechanism for Blockchain-Based Asynchronous Federated Learning

Tang, Y., Zhang, Z., Hu, J., & Qi, M. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-9412-6_36

Security, Privacy, and Communication Efficiency in Federated Learning: Issues and Perspectives

Otari, M. S., Kumar, B. S., & Patil, M. B. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-5791-6_19

Adaptive Client-Dropping in Federated Learning: Preserving Data Integrity in Medical Domains

Negrão, A., Silva, G., Pedrosa, R., Luz, E. J. D. S., & Silva, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79029-4_8

A Wolf in Sheep’s Clothing: Evading Robust Aggregation in Federated Learning through In-Distribution Attacks

McGaughey, R., del Rincón, J. M., & Alouani, I. (2024) · Cyber Research Conference - Ireland (Cyber-RCI) · doi:10.1109/cyber-rci60769.2024.10939959

MTP: A Minimal Task Perturbation Framework for Mitigating Poisoning Attacks in Federated Learning

Jiang, W., Su, T., Wang, G., & Han, D. (2024) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml63543.2024.10958024 · Cited by 1

Popular Content Prediction Through Adversarial Autoencoder Using Anonymised Data

Maia, D. V. D. A., Vilela, J. P., & Curado, M. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978648

Federated Learning for Smart Cities in Free Trade Area of China: Current Status, Application Cases, Challenges and Opportunities

Zuo, M. (2024) · International Conference on Big Data Economy and Information Management (BDEIM) · doi:10.1145/3724154.3724309

Resilient Distributed Optimization

Kuwaranancharoen, K., Xin, L., & Sundaram, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-14081-5.00072-6

Security Threats and Defence Mechanisms in Federated Learning: A Comprehensive Review

Ocran, A., & Effah, J. (2025) · doi:10.38124/ijisrt/25may617

Security and Privacy in Federated Process Mining

Shaikh, E., Mohammad, N., Tariq, Z., & McClean, S. I. (2024) · IEEE Consumer Life Tech (ICLT) · doi:10.1109/iclt63507.2024.11038637

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

Cross-validation based approach to mitigate poisoning attacks in federated learning

Kaushal, V., Chauhan, A., Kritik, & Sharma, S. (2025) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2025.2521662

DNN Layer Specialization Through Sequential Training for Applications With Smart Road-User Interactions

Mertens, J. S., Cafiso, S. D., Galluccio, L., Morabito, G., & Pappalardo, G. (2025) · IEEE Access · doi:10.1109/access.2025.3584461

A Selective Privacy-Preserving Federal Learning Framework

Zhou, Z., He, W., Xiong, L., & Yang, X. (2025) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs65393.2025.11069499

Data Leakage and Data Poisoning

Kobayashi, M. (2025) · Springer eBooks · doi:10.1007/978-981-96-9223-1_5 · Cited by 1

FedUDA: Towards a Novel Unfairness Distribution Attack Against Federated Learning Models

Wang, Z., Fu, X., Liu, L., Dai, F., & Ding, J. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-93257-1_12

Digital Watermarking for Secure Federated Learning in the Internet of Vehicles

Hang, B., Deng, Q., & Wen, Q. (2025) · International Conference on Transportation Information and Safety · doi:10.1109/ictis68762.2025.11214857

Blockchain Meets Federated Learning: A Comprehensive Survey on Smart Contract-driven Optimization for Aggregation, Security, and Scalability

Boulahiat, B., Trichni, S., & Omary, F. (2025) · IEEE Congress on Information Science and Technology (CiSt) · doi:10.1109/cist65886.2025.11224214

FedETE: Privacy-Preserving Federated Event Trigger Extraction

Hu, F., Chang, T., Wu, M., Dong, S., Zhou, J., Yin, J., & Wang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_6

FedPDFGuard: A Federated Learning Approach for Robust Detection of Malicious PDF Files

Zhou, P., Chong, D., Jiang, H., Zhang, J., & Dai, R. (2025) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon65708.2025.11235483

Guided by Noise: Vulnerable Poisoning Attack to Differentially Private Federated Learning

Dai, S., Hu, Y., Yu, H., Wang, H., & Wang, S. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161449

FusionMIA: Enhancing Membership Inference Attacks with Spy Clients and Shadow Models in Federated Learning

Lan, X., Li, J., Ying, Z., Han, X., & Xu, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3055-7_22

A robust federated aggregation algorithm for multimodal data in smart grid scenarios

He, D., Yan, J., Wang, Y., Fei, Z., Xia, Y., Li, H., & Wang, W. (2026) · Multimedia Systems · doi:10.1007/s00530-025-02070-3

B-DMTFL: A Practical and Efficient Framework of Dynamic Multi-Tier Federated Learning Based on Blockchain

Li, D., Chen, J. H., Xian, Y., Zhang, M., & Liu, P. (2026) · IEICE Transactions on Communications · doi:10.23919/transcom.2025ebp3121

A Survey on Blockchain-Based Federated Learning (BCFL) Frameworks

Lin, C.-Y., Wang, L.-J., & Kuo, T. T. (2026) · Springer handbooks of computational statistics · doi:10.1007/978-3-031-95418-4_12

Inversion, Membership, and Poisoning Attacks in Federated Learning - Challenges and Defenses

Farooqi, S. A., Rahman, A. A., & Saad, A. B. (2026) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom69009.2026.11360927

Causal Inference-Based Collaborative Membership Inference Attack on Federated Recommendation

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