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

Reliability and robustness of federated learning in practical applications

Fraboni, Y. (2023) · theses.fr (ABES) · doi:10.70675/17ed5fa7z21b0z4d8dz91cbzd14564ef336e

Secure collaborative management platform applied to IoT Networks

Atia, O. B. (2024) · theses.fr (ABES) · doi:10.70675/6c43d453z9fb0z4a37z8123z35304e4a31cc

A Resilient Federated Learning Framework for LLM Security in Healthcare

Annaiah, A. K. M., Perez, A., Riggan, M., & Rachakonda, L. (2025) · IEEE International Symposium on Smart Electronic Systems (iSES) · doi:10.1109/ises67504.2025.00077

Blockchain Integrated Federated Learning with Privacy-Preserving Neural Networks

Yarlagadda, K. C. (2026) · IEEE International Conference on Emerging Computing and Intelligent Technologies (ICoECIT) · doi:10.1109/icoecit68303.2026.11496782

Flower Gone? Exploring Insider Attacks and the Effects on Federated Learning

Copeland, A. M., & Meng, W. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21177-4_5

Federated learning with context-aware client collaboration: Challenges, advances, and open problems

Ziaeinejad, S., & Samet, S. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104446

FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning

Geimer, A., Pontiveros, B. B. F., & State, R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19102-1_27

K-means resilient to Byzantine faults

Fokoa, K. J. F., & Yonta, P. M. (2026) · Progress in Artificial Intelligence · doi:10.1007/s13748-026-00453-9

FedPD++: Enhanced Federated Open-Set Recognition with Parameter Disentanglement

Yang, C., Zhu, M., Liu, Y., & Yuan, Y. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02861-9

Federated Learning: A Comprehensive Review of Methods, Challenges, Applications and Trends

BashariRad, B., Choenni, S., & Omar, M. S. B. A. A. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1015368

Q-MA3DQN: Quantum-Secured Scheduling for Contact-Constrained Decentralized Satellite Federated Learning via Multiagent Quantum-Dueling Double Deep Q -Networks

Behera, B. K., Alhammad, S. M., Khalifa, A. A., Mumtaz, S., & Abulkasim, H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3693645

Data-Agnostic MP Techniques

Li, K., Yuan, X., & Ni, W. (2026) · Springer series in reliability engineering · doi:10.1007/978-3-032-23959-4_6

Federated Contrastive Diffusion Prototypes for Robust Private Learning

Zhang, J., Li, X., Liu, W., Li, C., Khan, M. K., & Chen, J. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3696206

DRFL: A Dual‐Encryption and Reputation Evaluation Defence Against Malicious Attacks on Federated Learning

Zheng, Y., Tian, Y., Zhou, Z., Wang, S., Li, M., & Xiong, J. (2026) · IET Communications · doi:10.1049/cmu2.70173

Federated Learning and Zero Trust Framework for Anomaly Detection in Distributed IIoT-Cloud Systems

Bata, F., Aljawarneh, M. M., Al-Na’amneh, Q., Hazaymih, R., & Alhazaimeh, A. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-4581-9.ch007

Fedgraphnet: a federated graph neural network framework for privacy-preserving traffic forecasting in heterogeneous IOT networks

Djumayozovna, Z. M. (2025) · Journal of Artificial Intelligence Machine Learning and Neural Network · doi:10.55529/jaimlnn.52.58.68

TrustHealth-FL: Federated learning-based secure authentication and data integrity framework for healthcare applications

Hegde, G., & Bhat, R. (2026) · Cogent Engineering · doi:10.1080/23311916.2026.2678666

RankBridge: Privacy-Preserving Rank-Based Explanation Clustering for Heterogeneous Federated Phishing Detection

Lim, P., Kumar, P., Zanni, R., & Lambdin, T. (2026) · Computation · doi:10.3390/computation14060137

FedHetero: improving federated learning on heterogeneous data distribution through deep reinforcement learning

Sun, S., Wang, J., Chen, J., Tang, X., Wang, S., Wang, S., Zheng, J., Huang, Y., Wang, J., Tsai, W.-T., et al. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03160-y

Security Architecture for Edge Computing under Partial Connectivity

Velusamy, P., & Rajkumar, R. (2026) · International Conference on Connected Intelligence for Industrial Applications (CI2A) · doi:10.1109/ci2a69097.2026.11576959

Enhancing Verifiable Federated Learning with Collusion-Resistant Verification

Wang, H., Wang, L.-P., Zhang, L. F., & Wang, H. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11581689

A Game Theory Dynamic Incentive Optimization Model for Decentralized Federated Learning

Chen, J. H., Li, D., Wang, P., Liu, P., Zhang, M., & Xian, Y. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582232

FL-PRCR: A Preserving Federated Learning Framework for Cross-Institutional Financial Risk Control with Interpretability and Compliance

Li, D. (2026) · Journal of big data and computing. · doi:10.62517/jbdc.202601214

FedRWA: A Robust Semi-asynchronous Federated Learning Framework via Dynamic Reputation-Weighted Aggregation

Zhang, T. A., Yang, L., Wang, J., Yuan, F., Zhang, C. L., & Kang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2862-1_13

Ingeniería de software aplicada al aprendizaje federado: estudio de bibliotecas y mecanismos de protección de datos

Rosado, G. A. H. (2025) · CognitivaTech Ingeniería de Software Inteligente y Sistemas Adaptativos · doi:10.63688/cognitivatech.v2.i1.30

A comprehensive review of temporal-contextual market-basket analysis and privacy-preserving federated association rule mining in cross-channel consumer behavior

Godela, R. (2026) · International Journal of Cloud Computing and Database Management · doi:10.33545/27075907.2026.v7.i1b.138

Privacy-Preserving Federated Learning for Heart Disease Prediction—A Review of Security Architectures and Techniques

Choudhari, A., & Bhalekar, M. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31216-7_7

FLVaccin: Unbalanced Hierarchical Federated Learning with Vaccination-Calibrated Adaptive Quarantine for Robust Poisoning Defense

David, T.-M., & Udrescu, M. (2026) · Computers · doi:10.3390/computers15080547

Parameter Masking Meets Temporal Smoothness in Backdoor Defense for Federated Learning

Jiang, H., Zhang, Z., Li, Z., Zhang, Y., & Li, Y. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-8447-5_5

Comparative Performance Evaluation of Six Federated Learning Frameworks Under Locked FedAvg: Native SDKs and a Shared Reference Harness for Edge-Oriented 6G Applications

Batsios, V. D., & Angelis, C. T. (2026) · Future Internet · doi:10.3390/fi18080416

Temporal Behavior Fingerprinting for Anomaly Detection in IoT Networks Using Sequence-Based Learning

Nutalapati, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31523-6_2

Fed-CBE: Client-Side Backdoor Elimination in Federated Learning via Persistent Parameter Disruption

Li, C., Shen, Y., Xie, M., Su, Z., Wang, Y., Zhang, C., & Zhu, L. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3723162

Air Quality Monitoring in Sparse Urban Networks: An Enhanced Fog Hierarchical Federated Learning Approach

Manju, A. B., Kumar, C. S. P., Nunna, S. K., Jegan, J., & Jagadeesan, D. (2026) · SN Computer Science · doi:10.1007/s42979-026-05273-1

A Federated Learning Driven Sustainable Computing Framework for Global Development Applications

Ghatkamble, R., M, P. K. G., Anandaram, H., Bhavanam, S. N., Balasubramani, R., & Naveenkumar, R. (2026) · International Conference on Emerging Trends in Information, Communication & Systems (ICETICS) · doi:10.1109/icetics69505.2026.11651270

DPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge Intelligence

Lyu, Y., Cao, J. P., Niu, B., Sun, Z., Wang, F., Li, F., & Li, H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3726505

PFAD-BC: privacy-preserving federated anomaly detection across multi-operator EV charging consortia via a blockchain-anchored framework

Aldweesh, A. Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01265-6

A Federated Autoencoder Framework for Cross-Payer Healthcare Billing Fraud Detection

Al-Mahdi, R., Al-Suwaidi, K., & Al-Kuwari, M. (2025) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/f467680042

The Efficiency-Decentralization-Security Trilemma: A Co-Design Framework for Lightweight, Decentralized AI in Cyber-Physical Systems

Ramadan, M. N. A., & Saygın, H. (2026) · AI · doi:10.3390/ai7090358

Lightweight adversarial membership inference attack with temporal evolution against federated learning

Liang, T., Wang, X., Yu, M., Yuan, S., & Guan, Z. (2026) · Digital Communications and Networks · doi:10.1016/j.dcan.2026.09.007

A Hybrid Machine Learning Platform for Securing Edge Computing Environments: A Review

Masendu, H. S., Pandey, J., Chiwariro, R., & Simango, D. (2026) · MEC International Conference on Big Data and Smart City (ICBDSC) · doi:10.1109/icadaai64945.2026.11679229

Navigating Security Concerns For IoT Resilience: Federated Learning Self-Healing Architecture for Attacks Detection Techniques

Ibrahim, H. M., Ismail, Y., & Alkhzaimi, H. A. (2026) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi69914.2026.11685847

A Gradient-Sensitivity-Driven Differential Privacy Mechanism for Federated Medical Image Diagnosis

Yu, J., Wang, H., & Wang, S. (2026) · International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) · doi:10.1109/aiotc71250.2026.11689493

Leveraging federated learning for smart grids: A comprehensive survey

Husnoo, M. A., Anwar, A., & Rahman, M. A. (2026) · Cyber-Physical Energy Systems · doi:10.1016/j.cpes.2026.07.002

Aggregation Algorithm Selection for Non-IID Federated Learning in Healthcare: Analysis of Convergence, Privacy, and Communication Tradeoffs

Zaffarullah, Z. R. A., Gudala, V. M., Shanthi, J. M., Srivastava, S., Srivastava, S., & Kumar, S. (2026) · International Conference on Secure Information Systems and Technologies (ICSIST) · doi:10.1109/icsist70215.2026.11706080

Evaluation of Label-Flipping Attacks on Federated Anomaly Detection in xG Networks

Bin-Ruba, S. A., Velloso, P. B., & Gu, P. (2026) · International Conference on Future and Intelligent Networking (FINE) · doi:10.1109/fine71157.2026.00052

Navigating the privacy-accuracy tradeoff: federated survival analysis with binning and differential privacy

Gouthamchand, V., van Soest, J., Arcuri, G., Dekker, A. L. A. J., Damiani, A., & Wee, L. (2026) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2026.1917462

Beyond Data Silos: A Comprehensive Survey on Privacy-Preserving and Secure Federated Learning

Goswami, C., Patil, S., -, R. K., Dhave, S., Kuthe, A., & Purva, T. (2026) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11716941

Secure Federated Data Sharing for Privacy-Sensitive Applications

Biplob, M. Y., Patkar, P., Mannava, M. K., Pasa, V. R., Jaladi, D. S., & Alam, M. S. (2026) · doi:10.1109/incect72370.2026.11742867

Threats to federated learning A survey

Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv · doi:10.48550/arxiv.2003.02133 · Cited by 221

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548

A survey on security and privacy threats to federated learning

Zhang, J., Li, M., Zeng, S., Xie, B., & Zhao, D. (2021) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana53684.2021.00062 · Cited by 34

A survey on security and privacy of federated learning

Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480

Data poisoning and adversarial attacks in federated learning

Jeelan, P. M., & Khan, K. N. (2026) · CRC Press eBooks · doi:10.1201/9781003660330-6