The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Challenges and future directions of secure federated learning: a survey
Open accessAbstract
Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning.Electronic supplementary material Supplementary material is available in the online version of this article at 10.1007/s11704-021-0598-z.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
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
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023
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
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
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
Research works printed but not linked: 7
- McMahan B, Moore E, Ramage D, Hampson S, Arcas y B A. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of Artificial Intelligence and Statistics. 2017, 1273–1282
- Bhagoji A N, Chakraborty S, Mittal P, Calo S. Analyzing federated learning through an adversarial lens. In: Proceedings of International Conference on Machine Learning. 2019, 634–643
- Konečnỳ J, McMahan H B, Yu F X, Richtarik P, Suresh A T, Bacon D. Federated learning: strategies for improving communication efficiency. In: Proceedings of NIPS Workshop on Private Multi-Party Machine Learning. 2016
- Rakhlin A, Shamir O, Sridharan K. Making gradient descent optimal for strongly convex stochastic optimization. In: Proceedings of the 29th International Coference on International Conference on Machine Learning. 2012, 1571–1578
- Biggio B, Nelson B, Laskov P. Poisoning attacks against support vector machines. In: Proceedings of the 29th International Coference on International Conference on Machine Learning. 2012, 1467–1474
- Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V. How to backdoor federated learning. In: Proceedings of International Conference on Artificial Intelligence. 2020, 2938–2948
- Dinh C T, Tran N, Nguyen T D. Personalized federated learning with moreau envelopes. Advances in Neural Information Processing Systems, 2020: 33
Cited by
Sorted by citations
A survey on federated learning: challenges and applications
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01647-y · Cited by 1,014
Xu, M., Ng, W. C., Lim, W. Y. B., Kang, J., Xiong, Z., Niyato, D. T., Yang, Q., Shen, X., & Miao, C. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3221119 · Cited by 614
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
The Impact of Adversarial Attacks on Federated Learning: A Survey
Kumar, K. N., Mohan, C. K., & Cenkeramaddi, L. R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3322785 · Cited by 209
Federated Learning for Edge Computing: A Survey
Brecko, A., Kajáti, E., Koziorek, J., & Zolotová, I. (2022) · Applied Sciences · doi:10.3390/app12189124 · Cited by 181
Metadata
- Authors
- Zhang, K., Song, X., Zhang, C., & Yu, S.
- Affiliations
- University of Technology Sydney (AU)
- Southern University of Science and Technology (CN)
- Corresponding author
- Song, Xuan
- Yu, Shui
- Venue
- Frontiers of Computer Science
- Published
- 2021-12-10
- Volume
- 16
- Issue
- 5
- Pages
- 165817-165817
- ISSN
- 2095-2228 (ISSN-L)
- 2095-2236
- PMC
- PMC8663756
- OpenAlex
- W4200419653
- Cited by
- 127
- References
- 47
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 43 · 2023: 29 · 2022: 14 · 2021: 1
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Frontiers of Computer Science (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- federated learning, privacy preservation, secure federated learning, model parameter aggregation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
