| [1] |
Zhang C, Xie Y, Bai H, Yu B, Li W, et al. 2021. A survey on federated learning. |
| [2] |
Rieke N, Hancox J, Li W, Milletarì F, Roth HR, et al. 2020. The future of digital health with federated learning. |
| [3] |
Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, et al. 2020. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. |
| [4] |
Albshaier L, Almarri S, Albuali A. 2025. Federated learning for cloud and edge security: a systematic review of challenges and ai opportunities. |
| [5] |
Geiping J, Bauermeister H, Dröge H, Moeller M. 2020. Inverting gradients − how easy is it to break privacy in federated learning? 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020, Vancouver, Canada. pp. 16937−16947 https://proceedings.neurips.cc/paper_files/paper/2020/file/c4ede56bbd98819ae6112b20ac6bf145-Paper.pdf |
| [6] |
Yin H, Mallya A, Vahdat A, Alvarez JM, Kautz J, et al. 2021. See through gradients: Image batch recovery via gradinversion. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20−25 June 2021, Nashville, TN, USA. USA. IEEE. pp. 16332−16341 doi: 10.1109/cvpr46437.2021.01607 |
| [7] |
Xu W, Yang H, Zhang M, Pan X, Liu W, et al. 2022. Retinal vessel segmentation with vae reconstruction and multi-scale context extractor. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India, 28−31 March 2022. USA: IEEE. pp. 1−5 doi: 10.1109/ISBI52829.2022.9761563 |
| [8] |
Lim WYB, Luong NC, Hoang DT, Jiao Y, Liang YC, et al. 2020. Federated learning in mobile edge networks: a comprehensive survey. |
| [9] |
Adam M, Albaseer A, Baroudi U, Abdallah M. 2025. Survey of multimodal federated learning: Exploring data integration, challenges, and future directions. |
| [10] |
Wang Z, Ma J, Wang X, Hu J, Qin Z, et al. 2022. Threats to training: a survey of poisoning attacks and defenses on machine learning systems. ACM Computing Surveys 55(7):1−36 |
| [11] |
Li Y, Guo Z, Yang N, Chen H, Yuan D, et al. 2025. Threats and defenses in the federated learning life cycle: a comprehensive survey and challenges. |
| [12] |
Sun J, Li A, DiValentin L, Hassanzadeh A, Chen Y, et al. 2021. FL-WBC: enhancing robustness against model poisoning attacks in federated learning from a client perspective. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021, Virtual. USA: NeurIPS. pp. 12613−12624 https://proceedings.neurips.cc/paper_files/paper/2021/file/692baebec3bb4b53d7ebc3b9fabac31b-Paper.pdf |
| [13] |
Shejwalkar V, Houmansadr A, Kairouz P, Ramage D. 2022. Back to the drawing board: a critical evaluation of poisoning attacks on production federated learning. 2022 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 22−26 May 2022. USA: IEEE. pp. 135422−135426 doi: 10.1109/SP46214.2022.9833647 |
| [14] |
Goldblum M, Tsipras D, Xie C, Chen X, Schwarzschild A, et al. 2023. Dataset security for machine learning: data poisoning, backdoor attacks, and defenses. |
| [15] |
Peng J, Xing H, Xu L, Luo S, Dai P, et al. 2024. Adversarial reinforcement learning based data poisoning attacks defense for task-oriented multi-user semantic communication. |
| [16] |
Uddin MP, Xiang Y, Hasan M, Bai J, Zhao Y, et al. 2025. A systematic literature review of robust federated learning: issues, solutions, and future research directions. |
| [17] |
Singh J, Singh P, Kaur A, Hedabou M. 2025. Post-quantum secure fog-edge computing using federated learning with blockchain. |
| [18] |
Li X, Qu Z, Zhao S, Tang B, Lu Z, et al. 2021. LoMar: a local defense against poisoning attack on federated learning. |
| [19] |
Lyu L, Yu H, Ma X, Chen C, Sun L, et al. 2022. Privacy and robustness in federated learning: attacks and defenses. |
| [20] |
Shejwalkar V, Houmansadr A. 2021. Manipulating the byzantine: optimizing model poisoning attacks and defenses for federated learning. Network and Distributed Systems Security (NDSS) Symposium 2021, 21−25 February 2021, Virtual. Reston, VA, USA: The Internet Society. pp. 1−18 doi: 10.14722/ndss.2021.24498 |
| [21] |
Zhao B, Sun P, Wang T, Jiang K. 2022. FedInv: Byzantine-robust federated learning by inversing local model updates. |
| [22] |
Ma C, Li J, Wei K, Liu B, Ding M, et al. 2023. Trusted ai in multiagent systems: an overview of privacy and security for distributed learning. |
| [23] |
Nguyen TD, Rieger P, Chen H, Yalame H, Möllering H, et al. 2022. FLAME: taming backdoors in federated learning. 31st USENIX Security Symposium (USENIX Security 22), August 10–12, 2022, Boston, MA, USA. Berkeley, CA, USA: USENIX Association. pp. 1415–1432 www.usenix.org/system/files/sec22-nguyen.pdf |
| [24] |
Wang H, Sreenivasan K, Rajput S, Vishwakarma H, Agarwal S, et al. 2020. Attack of the tails: yes, you really can backdoor federated learning. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020. Vancouver, Canada. pp. 16070−16084 https://proceedings.neurips.cc/paper_files/paper/2020/file/b8ffa41d4e492f0fad2f13e29e1762eb-Paper.pdf |
| [25] |
Xu Z, Jiang F, Niu L, Jia J, Li B, et al. 2024. ACE: a model poisoning attack on contribution evaluation methods in federated learning. 33rd USENIX Security Symposium (USENIX Security 24), August 14–16, 2024, Philadelphia, PA, USA. Berkeley, CA, USA: USENIX Association. pp. 4175–4192 www.usenix.org/system/files/usenixsecurity24-xu-zhangchen.pdf |
| [26] |
Yu H, Xu R, Zhang H, Yang Z, Liu H. 2024. EV-FL: efficient verifiable federated learning with weighted aggregation for industrial iot networks. |
| [27] |
Fang M, Cao X, Jia J, Gong NZ. 2020. Local model poisoning attacks to Byzantine-Robust federated learning. 29th USENIX Security Symposium (USENIX Security 20), August 12–14, 2020, Virtual. Berkeley, CA, USA: USENIX Association. pp. 1605–1622 https://www.usenix.org/system/files/sec20-fang.pdf |
| [28] |
Cao X, Gong NZ. 2022. MPAF: model poisoning attacks to federated learning based on fake clients. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, June 19−20, 2022, New Orleans, LA, USA. USA: IEEE. pp. 3395−3403. 10.1109/cvprw56347.2022.00383 |
| [29] |
Xie Y, Fang M, Gong NZ. 2025. Model poisoning attacks to federated learning via multi-round consistency. In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR). June 10−17, 2025. Nashville, TN, USA. USA: IEEE. pp. 15454−15463 doi: 10.1109/cvpr52734.2025.01440 |
| [30] |
Lyu X, Han Y, Wang W, Liu J, Wang B, et al. 2024. CoBA: collusive backdoor attacks with optimized trigger to federated learning. |
| [31] |
Chen H, Fu C, Zhao J, Koushanfar F. 2021. ProFlip: Targeted trojan attack with progressive bit flips. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). October 10−17, 2021. Montreal, QC, Canada. USA: IEEE. pp. 7698−7707 doi: 10.1109/iccv48922.2021.00762 |
| [32] |
Li H, Ye Q, Hu H, Li J, Wang L, et al. 2023. 3DFed: adaptive and extensible framework for covert backdoor attack in federated learning. 2023 IEEE Symposium on Security and Privacy (SP). May 21−25, 2023. San Francisco, CA, USA. USA: IEEE. pp. 1893−1907. 10.1109/sp46215.2023.10179401 |
| [33] |
Xu J, Zhang Z, Hu R. 2025. Detecting backdoor attacks in federated learning via direction alignment inspection. Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR). June 10−17, 2025, Nashville, TN, USA. USA: IEEE. pp. 20654−20664 doi: 10.1109/CVPR52734.2025.01923 |
| [34] |
Ding B, Yang P, Huang SJ. 2025. FLAIN: Mitigating backdoor attacks in federated learning via flipping weight updates of low-activation input neurons. Proceedings of the 2025 International Conference on Multimedia Retrieval, Chicago IL USA, 2025. USA: ACM. pp. 219−227 doi: 10.1145/3731715.3733342 |
| [35] |
Zheng J, Li K, Yuan X, Ni W, Tovar E. 2024. Detecting poisoning attacks on federated learning using gradient-weighted class activation mapping. Companion Proceedings of the ACM Web Conference 2024, May 13−17, 2024, Singapore, Singapore. USA: ACM. pp. 714−717 doi: 10.1145/3589335.3651490 |
| [36] |
Xu W, Yang H, Zhang M, Cao Z, Pan X, et al. 2022. Brain tumor segmentation with corner attention and high-dimensional perceptual loss. |
| [37] |
, Zhang Z, Cao X, Jia J, Gong NZ. 2022. FLDetector: defending federated learning against model poisoning attacks via detecting malicious clients. Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, August 14−18, 2022, Washington DC, USA. USA: ACM. pp. 2545−2555 doi: 10.1145/3534678.3539231 |
| [38] |
Kabir E, Song Z, Ur Rashid MR, Mehnaz S. 2024. FLShield: a validation based federated learning framework to defend against poisoning attacks. 2024 IEEE Symposium on Security and Privacy (SP), May 19−23, 2024, San Francisco, CA, USA. USA: IEEE. pp. 2572−2590 doi: 10.1109/SP54263.2024.00141 |
| [39] |
Fereidooni H, Pegoraro A, Rieger P, Dmitrienko A, Sadeghi AR. 2024. FreqFed: a frequency analysis-based approach for mitigating poisoning attacks in federated learning. Network and Distributed System Security (NDSS) Symposium 2024, 26 February − 1 March 2024, San Diego, CA, USA. pp. 1−16 doi: https://dx.doi.org/10.14722/ndss.2024.23620 |
| [40] |
Rieger P, Nguyen TD, Miettinen M, Sadeghi AR. 2022. DeepSight: mitigating backdoor attacks in federated learning through deep model inspection. Network and Distributed System Security (NDSS) Symposium 2022, 24-28 April 2022, San Diego, CA, USA. doi: https://dx.doi.org/10.14722/ndss.2022.23156 |
| [41] |
Shan X, Yu H, Chen Y, Chen Y, Yang Z. 2025. S2A-P2FS: secure storage auditing with privacy-preserving flexible data sharing in cloud-assisted industrial iot. |
| [42] |
Li T, Hu S, Beirami A, Smith V. 2021. Ditto: fair and robust federated learning through personalization. The 38th International Conference on Machine Learning (ICML 2021), 18–24 July 2021, Virtual. Vol. 139. Online: PMLR. pp. 6357–6368 https://proceedings.mlr.press/v139/li21h/li21h.pdf |
| [43] |
Bell J, Gascón A, Lepoint T, Li B, Meiklejohn S, et al. 2023. ACORN: input validation for secure aggregation. The 32nd USENIX Security Symposium (USENIX Security ’23), from 9–11 August 2023, Anaheim, CA, USA. Berkeley, CA, USA: USENIX Association. pp. 4805−4822 www.usenix.org/system/files/usenixsecurity23-bell.pdf |
| [44] |
Ma Y, Guo Y, Karthikeyan H, Polychroniadou A. 2025. Armadillo: robust single-server secure aggregation for federated learning with input validation. Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security (CCS ’25), October 13–17, 2025, Taipei, Taiwan. New York, NY, USA: Association for Computing Machinery. pp. 2219–2233 doi: 10.1145/3719027.3765216 |
| [45] |
Roy Chowdhury A, Guo C, Jha S, van der Maaten L. 2022. Eiffel: ensuring integrity for federated learning. Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022, Los Angeles, CA, USA. USA: ACM. pp. 2535−2549 doi: 10.1145/3548606.3560611 |
| [46] |
Xu W, Yang H, Zhang M, Pan X, Liu W, et al. 2021. DECNet: a dual-stream edge complementary network for retinal vessel segmentation. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), December 9−12, 2021. Houston, TX, USA. USA: IEEE. pp. 1595−1600 doi: 10.1109/bibm52615.2021.9669751 |
| [47] |
Shan X, Yu H, Chen Y, Yang Z. 2023. Physical unclonable functionbased lightweight and verifiable data stream transmission for industrial iot. |
| [48] |
Rathee M, Shen C, Wagh S, Popa RA. 2023. ELSA: secure aggregation for federated learning with malicious actors. 2023 IEEE Symposium on Security and Privacy (SP), May 21−25, 2023, San Francisco, CA, USA. USA: IEEE. pp. 1961−1979 doi: 10.1109/SP46215.2023.10179468 |
| [49] |
Chen H, Liu H, Wu Z, Fan X, Cao L. 2025. FedSi: federated subnetwork inference for efficient uncertainty quantification. |
| [50] |
Tian Y, Chen H, Zhu X, Yu X. 2026. FedKT: a nature-inspired bayesian federated learning framework for privacy-preserving and lightweight consumer devices. |