A Taxonomy of Attacks on Federated Learning

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

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

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

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

MixMatch: A Holistic Approach to Semi-Supervised LearningPreprint

Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02249 · Cited by 2,523

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

How to backdoor federated learning

Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2018.00027 · Cited by 1,001

The security of machine learning

Barreno, M., Nelson, B. A., Joseph, A. D., & Tygar, J. D. (2010) · Machine Learning · doi:10.1007/s10994-010-5188-5 · Cited by 839

Adaptive federated learning scheme for recognition of malicious attacks in an IoT network

Chhikara, P., Tekchandani, R., & Kumar, N. (2023) · Computing · doi:10.1007/s00607-022-01146-6 · Cited by 9

Healthcare federated learning: a survey of applications and frameworks

Pais, V., Rao, S., & Muniyal, B. (2025) · International Journal of Computers and Applications · doi:10.1080/1206212x.2025.2496913 · Cited by 9

VFL-R: a novel framework for multi-party in vertical federated learning

Li, J., Yan, T., & Ren, P. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04111-0 · Cited by 8

Rectification of Syntactic and Semantic Privacy Mechanisms

Majeed, A., & Hwang, S. O. (2022) · IEEE Security & Privacy · doi:10.1109/msec.2022.3188365 · Cited by 8

SAFe‐Health: Guarding federated learning‐driven smart healthcare with federated defense averaging against data poisoning

Mali, B., Singh, P. K., & Mazumdar, N. (2024) · Security and Privacy · doi:10.1002/spy2.403 · Cited by 7

Securing the collective intelligence: a comprehensive review of federated learning security attacks and defensive strategies

Kaushal, V., & Sharma, S. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02339-z · Cited by 9

Comparing Different Membership Inference Attacks With a Comprehensive Benchmark

Niu, J., Zhu, X., Zeng, M., Zhang, G., Zhao, Q., Huang, C., Zhang, Y., An, S., Wang, Y., Yue, X., et al. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3550070 · Cited by 9

The latent space of data ethics

Panai, E. (2023) · Ai & Society · doi:10.1007/s00146-023-01757-3 · Cited by 5

BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning

Liu, Y., Wang, C., & Yuan, X. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671879 · Cited by 7

DWAMA: Dynamic weight-adjusted mahalanobis defense algorithm for mitigating poisoning attacks in federated learning

Zhang, G., Liu, H., Yang, B. T., & Feng, S. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01794-9 · Cited by 7

Enhancing Security and Privacy in Federated Learning Using Low-Dimensional Update Representation and Proximity-Based Defense

Li, W., Fan, K., Zhang, J., Li, H., Lim, W. Y. B., & Yang, Q. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3539717 · Cited by 9

MILSA: Model Interpretation Based Label Sniffing Attack in Federated Learning

Manna, D., Kasyap, H., & Tripathy, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23690-7_8 · Cited by 7

Local perturbation-based black-box federated learning attack for time series classification

Chen, S., Yuan, J., Wang, Z., & Sun, Y. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.048 · Cited by 6

Trustworthy AI Using Confidential Federated Learning

Guo, J., Pietzuch, P. R., Paverd, A., & Vaswani, K. (2024) · Communications of the ACM · doi:10.1145/3677390 · Cited by 6

Federated TSRN-Enabled GANs for Effective Cyber Attack Detection in Edge Computing

Sharmin, Z., Xiang, Y., Uddin, M. P., Chen, F., Zhang, Y., & Tang, J. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3576322 · Cited by 6

Benefits of the Federation? Analyzing the Impact of Fair Federated Learning at the Client Level

Corbucci, L., Heilmann, X., & Cerrato, M. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732152 · Cited by 9

Adversarial Machine Learning: A 20-Year Survey of Attacks, Defenses, and Standards

Tekeste, B., Al-Hussaeni, K., Fung, B. C. M., Alawadhi, I. M., & Fachkha, C. (2026) · IEEE Access · doi:10.1109/access.2026.3690620 · Cited by 7

A Comprehensive Review of Artificial Intelligence and Machine Learning Methods for Modern Healthcare Systems

Ahmed, K. M., Das, B. C., Saadati, Y., & Amini, M. H. (2024) · Big and integrated artificial intelligence · doi:10.1007/978-3-031-57567-9_4 · Cited by 6

DM-FedMF: A Recommendation Model of Federated Matrix Factorization With Detection Mechanism

Zheng, X., Jia, X., Cheng, X., He, W., Sun, L., Guo, L., Yu, Q., & Luo, Y. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3551923 · Cited by 6

Proof of Federated Training: Accountable Cross-Network Model Training and Inference

Chakraborty, S., & Chakraborty, S. (2022) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc54727.2022.9805548 · Cited by 6

Balanced Energy Consumption Based on Historical Participation of Resource-Constrained Devices in Federated Edge Learning

Albaseer, A. M., Abdallah, M. M., Al‐Fuqaha, A., & Erbad, A. (2022) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc55113.2022.9824246 · Cited by 6

TrustFed: A Reliable Federated Learning Framework With Malicious-Attack Resistance

Hang, S., Zhou, J., Feng, G., & Niu, X. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3510567 · Cited by 9

Federated learning security mechanisms for protecting sensitive data

Al-Ameer, A. A. A., & Bhaya, W. S. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/beei.v12i4.4751 · Cited by 7

AIDFL: An Information-Driven Anomaly Detector for Data Poisoning in Decentralized Federated Learning

Chen, X., Feng, C., & Wang, S. (2025) · IEEE Access · doi:10.1109/access.2025.3552168 · Cited by 5

On the local convergence of ADAM-DPGAN with simultaneous and alternating gradient decent training methods

Azadmanesh, M., Ghahfarokhi, B. S., & Talouki, M. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125646 · Cited by 5

TIMPANY—deTectIon of Model Poisoning Attacks usiNg accuracY

Sameen, M., & Hwang, S. O. (2021) · IEEE Access · doi:10.1109/access.2021.3118926 · Cited by 5

Privacy Analysis of Federated Learning via Dishonest Servers

Jeter, T. R., & Thai, M. T. (2023) · IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) · doi:10.1109/bigdatasecurity-hpsc-ids58521.2023.00015 · Cited by 6

Novel Approach for Curbing Unfair Energy Consumption and Biased Model in Federated Edge Learning

Albaseer, A. M., Seid, A. M., Abdallah, M. M., Al‐Fuqaha, A., & Erbad, A. (2024) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2024.3350735 · Cited by 7

SaFL: Sybil-aware Federated Learning with Application to Face Recognition

Ghafourian, M., Fiérrez, J., Vera-Rodríguez, R., Tolosana, R., & Morales, A. (2024) · IEEE International Conference on Image Processing Workshops (ICIPW) · doi:10.1109/icipcw64161.2024.10769131 · Cited by 5

Proceedings of the 5th workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems

(2023) · doi:10.1145/3584684 · Cited by 5

Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions

Hartsock, I., Koutsoubis, N., Ahmed, S., Parker, N. H., Schabath, M. B., Araujo, C. R., Qayyum, A., Lam, C. A., Gatenby, R. A., & Rasool, G. (2026) · Cancers · doi:10.3390/cancers18060942 · Cited by 5

Integrating Federated Learning and Differential Privacy for Secure Anomaly Detection in Smart Grids

Mohammadi, M., Shrestha, R., & Sinaei, S. (2024) · International Conference on Cloud and Big Data Computing (ICCBDC) · doi:10.1145/3694860.3694869 · Cited by 4

Defense-Guided Adaptive Attack on Byzantine-Robust Federated Learning

Hu, C., Liu, Y., Zhang, M., & Yang, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0151-6_7 · Cited by 4

Systemization of Knowledge: Robust Deep Learning using Hardware-software co-design in Centralized and Federated Settings

Zhang, R., Hussain, S., Chen, H., Javaheripi, M., & Koushanfar, F. (2023) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3616868 · Cited by 5

Technology for Developing a Unified Security Policy in Heterogeneous Big Data Systems

Poltavtseva, M. A., Zeghda, D., & Kalinin, M. (2024) · doi:10.1109/ips62349.2024.10499286 · Cited by 4

Coordinate-Wise Median in Byzantine Federated Learning

Cambus, M., Melnyk, D., Milentijević, T., & Schmid, S. (2025) · doi:10.1145/3709023.3737691 · Cited by 4

Campus Network Intrusion Detection based on Federated Learning

Chen, J., Guo, Q., Fu, Z., Shang, Q., Ma, H., & Wu, D. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892843 · Cited by 4

On Attacks To Federated Learning and a Blockchain-empowered Protection

Esposito, C., Sperlí, G., Moscato, V., & Zhao, Z. (2022) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc49033.2022.9700723 · Cited by 4

Privacy and security for 6G networks via Fog/Edge Computing, Blockchain, and Federated Learning: A survey and taxonomy

Valdez, W., Parra-Ullauri, J. M., Simeonidou, D. E., & Kertész, A. (2026) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2026.104510 · Cited by 3

A novel hybrid approach to detect clients in federated learning for industrial internet of things

Qammar, A., Wang, H., Ding, J., Naouri, A., Khelloufi, A., & Ning, H. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02753-3 · Cited by 3

Invited Paper: Enhancing Privacy in Federated Learning via Early Exit

Wu, Y., Chiasserini, C. F., Malandrino, F., & Levorato, M. (2023) · Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for Distributed Systems (ApPLIED) · doi:10.1145/3584684.3597274 · Cited by 3

CGAPP: A continuous group authentication privacy-preserving platform for industrial scene

Espín, J. M., Celdrán, A. H., Esquembre, F., Pérez, G. M., & Marı́n-Blázquez, J. G. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103622 · Cited by 4

Mitigating Gradient Inversion Attacks in Federated Learning with Frequency Transformation

Palihawadana, C., Wiratunga, N., Kalutarage, H., & Wijekoon, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-54129-2_44 · Cited by 3

Model Linkage Selection for Cooperative LearningPreprint

Zhou, J., Ding, J., Tan, K. M., & Tarokh, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07342 · Cited by 3

Resilient Federated Learning Using Trimmed-Clipping Aggregation

Bhowmick, C., & Koutsoukos, X. (2024) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa62245.2024.00031 · Cited by 3

Catalyst: Asynchronous Byzantine-Robust Federated Learning

Cox, B., Mălan, A., Chen, L. Y., & Decouchant, J. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402147 · Cited by 2

uMK-HEFL: Unconstrained multi-key homomorphic encryption for privacy-preserving federated learning

Liu, W., Wang, X. A., Zhao, W., Wu, L., Lei, H., & Jiang, W. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114997 · Cited by 2

FedAgent-Chain: A Secure Federated and Agentic AI Framework for Multilingual Disability-Inclusive Employment in AI Cities

Syed, T. A., Siddiqui, M. S., Akarma, A., & Formisano, A. (2026) · Smart Cities · doi:10.3390/smartcities9070106 · Cited by 2

Anomaly-Aware Federated Learning with Heterogeneous Data

Chen, Z., Hu, C.-H., & Larsson, E. G. (2021) · IEEE International Conference on Autonomous Systems (ICAS) · doi:10.1109/icas49788.2021.9551122 · Cited by 2

MLChain: a privacy-preserving model learning framework using blockchain

Bansal, V., Baliyan, N., & Ghosh, M. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00754-3 · Cited by 2

Trustworthy AI using Confidential Federated Learning

Guo, J., Pietzuch, P. R., Paverd, A., & Vaswani, K. (2024) · Queue · doi:10.1145/3665220 · Cited by 2

PRIV-DRIVE: Privacy-Ensured Federated Learning using Homomorphic Encryption for Driver Fatigue Detection

Sinaei, S., Mohammadi, M., Shrestha, R., Alibeigi, M., & Eklund, D. (2024) · 2024 27th Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd64264.2024.00063 · Cited by 2

FDBA: Feature-guided Defense against Byzantine and Adaptive attacks in Federated Learning

Hu, C., Hu, Q., Zhang, M., & Yang, Z. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104035 · Cited by 2

Guarding the Privacy of Label-Only Access to Neural Network Classifiers via iDP Verification

Kabaha, A., & Drachsler-Cohen, D. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3720480 · Cited by 1

Mitigating Evasion Attacks in Federated Learning Based Signal Classifiers

Wang, S., Sahay, R., Piaseczny, A., & Brinton, C. G. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3566954 · 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

Noise-robust and resource-efficient ADMM-based federated learning for WLS regression

Lari, E., Arablouei, R., Gogineni, V. C., & Werner, S. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.110387 · Cited by 2

Federated Pseudo-Labeling: A Data-Centric, Privacy-Preserving Framework for Medical Image Segmentation

Montaha, S., Rahman, R., Ghosh, T., Sheikhi, F., & Maleki, F. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3622934 · Cited by 2

Beyond data sharing: enhancing IoT intrusion detection with blockchain-enabled federated learning

Naik, A., & Shukla, R. M. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1770179 · Cited by 1

DECML: Distributed Edge Consensus Machine Learning Framework

Verdeyen, C., Shepherd, C., Markantonakis, K., Akram, R. N., Milroy, R., Ghazalah, S. A., & Sauveron, D. (2024) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot63427.2024.10833588 · 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

Federated Trust-Based Authentication for Secure Mobile Cloud Access

Thinh, L. V. (2025) · Journal of Science and Technology on Information security · doi:10.54654/isj.v1i24.1113 · Cited by 1

Approximate Agreement Algorithms for Byzantine Collaborative Learning

Cambus, M., Melnyk, D., Milentijević, T., & Schmid, S. (2025) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/3694906.3743343 · Cited by 1

On the trustworthiness of federated learning models for 5G network intrusion detection under heterogeneous data

Lamprou, V., Doukas, G. S., Ntanos, C., & Askounis, D. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111598 · Cited by 1

A Comparative Analysis of Label Flipping and Backdoor Data Poisoning Attacks in Federated Learning

Bisna, N. D., Navaneeth, D., & James, A. (2025) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11171343 · Cited by 1

Mitigating distributed denial of service-based cyberattack in federated computing framework using deep reinforcement learning with frilled lizard algorithm

Maghrabi, L. A., Ragab, M., Alghamdi, B. M., Alorfi, A. S., Hamed, D., Alharbi, A., & ALGhamdi, A. S. A.-M. (2025) · Scientific Reports · doi:10.1038/s41598-025-23899-8 · 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

Enhancing Employee Retention Prediction Through Federated Learning and Associative Classification using FedCBA: A Case Study on IBM HR Analytics

Büyüktanır, B., Baydoğmus, G. K., Ülkü, E. E., & Yıldız, K. (2026) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-026-11252-8 · Cited by 1

Privacy-preserving Federated Learning System for Fatigue Detection

Mohammadi, M., Allocca, R., Eklund, D., Shrestha, R., & Sinaei, S. (2023) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10224953 · Cited by 1

FedSec: Advanced Threat Detection System for Federated Learning Frameworks

Avishka, K. P. S., Gunasekara, G. K. A., Maduwantha, K. K., Srilal, J. P. L. C., Yapa, K., & Ruggahakotuwa, L. (2023) · International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac60630.2023.10417589 · 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

Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

Niu, J., Liu, P., Huang, C., Zhang, Y., Zeng, M., Shen, K., Wang, Y., An, S., Shen, Y., Jiang, X., et al. (2024) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2024.06.002 · Cited by 1

A Dynamic Optimization Scheme Based Backdoor Attack DPBA in Federated Learning

Yang, C., & Zhao, H. (2024) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat62382.2024.10695409 · Cited by 1

A Privacy-Preserving Decentralized Federated Learning Framework Based on Differential Privacy

Wang, L., Zhang, D., Guo, M., & Shao, X. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00078 · Cited by 1

Anomalous Client Detection in Federated Learning

Thakur, D., Guzzo, A., & Fortino, G. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-87639-4_8 · Cited by 1

Security and Privacy of 6G

de Alwis, C., Pham, Q., & Liyanage, M. (2022) · Wiley eBooks · doi:10.1002/9781119862321.ch10 · Cited by 2

Securing Federated Learning Model Aggregation Against Poisoning Attacks via Credit-Based Client Selection

Khorramfar, M. (2023) · doi:10.36939/ir.202308301626 · Cited by 1

Collaborative learning

Innocenti, L., Antonelli, M., Sebastien Ourselin, A., & Lorenzi, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00034-1 · Cited by 1

Erratum [for "A Taxonomy of Attacks on Federated Learning"]

(2021) · IEEE Security & Privacy · doi:10.1109/msec.2021.3065793

Balanced Energy Consumption Based on Historical Participation of Resource-Constrained Devices in Federated Edge LearningPreprint

Albaseer, A. M., Abdallah, M. M., Al‐Fuqaha, A., & Erbad, A. (2022) · doi:10.36227/techrxiv.19697293

Balanced Energy Consumption Based on Historical Participation of Resource-Constrained Devices in Federated Edge LearningPreprint

Albaseer, A. M., Abdallah, M. M., Al‐Fuqaha, A., & Erbad, A. (2022) · doi:10.36227/techrxiv.19697293.v1

KÜNSTLICHE INTELLIGENZ IM SICHERHEITSSYSTEM VON UNTERNEHMEN

Rochenovich, G.-D., Lisovskyi, K., & Butko, I. (2022) · ГРААЛЬ НАУКИ · doi:10.36074/grail-of-science.29.04.2022.051

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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

An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack

Bhatnagar, K., Chattanathan, S., Dang, A., Eranki, B., Rana, R., Sridhar, C., Vedam, S., Yao, A., & Stamp, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-83157-7_15 · Cited by 1

Mitigating Model Poisoning Attacks in Federated Learning: A Comprehensive Approach

Megha, R., Thampi, A. A., Varma, V. B. C., Yamuna, K., & Sarath, G. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-1185-0_18

A Unified Solution to Diverse Heterogeneities in One-Shot Federated Learning

Bai, J., Song, Y., Wu, D., Sajjanhar, A., Xiang, Y., Zhou, W., Tao, X., Li, Y., & Li, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736825 · Cited by 2

When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning

Sun, R., Hu, H., Luo, W., Zhang, Z., Zhang, Y., Yuan, H., & Zhang, L. Y. (2025) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3708821.3733915

July 2025 - Top 10 Read Articles in International Journal of Software Engineering & Applications (IJSEA)Preprint

anon, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5381013

Efficient Energy Theft Detection Utilizing Hierarchical Federated Learning

Shahid, S. S., Salman, T., Baza, M., & Srivastava, G. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106795

Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions

Qammar, A., Ding, J., & Ning, H. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10098-w · Cited by 58

Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives

Liu, P., Xu, X., & Wang, W. (2022) · Cybersecurity · doi:10.1186/s42400-021-00105-6 · Cited by 261

Security and Privacy in Federated Learning: A Survey and Unified Taxonomy of Threat Models, Attacks, and DefensesPreprint

Fenoglio, D., Gjoreski, M., Gabrielli, E., & Langheinrich, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6771005

Poisoning Attacks and Defenses in Federated Learning: A SurveyPreprint

Sagar, S., Li, C.-S., Loke, S. W., & Choi, J. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.05795 · Cited by 8

Security and privacy concerns in Federated Learning systems: a systematic reviewPreprint

Salvo, R. D., & Galletta, A. (2025) · Research Square · doi:10.21203/rs.3.rs-7148932/v1 · Cited by 1