On the security and privacy of federated learning: A survey with attacks, defenses, frameworks, applications, and future directions
- DOI
- 10.1016/j.inffus.2026.104155
- Published
- 2026-07
- Container
- Information Fusion
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.inffus.2026.104155,
title = {On the security and privacy of federated learning: A survey with attacks, defenses, frameworks, applications, and future directions},
author = {Daniel M. Jimenez-Gutierrez and Yelizaveta Falkouskaya and José L. Hernandez-Ramos and Aris Anagnostopoulos and Ioannis Chatzigiannakis and Andrea Vitaletti},
year = {2026},
journal = {Information Fusion},
doi = {10.1016/j.inffus.2026.104155},
url = {https://doi.org/10.1016/j.inffus.2026.104155}
}RIS
TY - JOUR TI - On the security and privacy of federated learning: A survey with attacks, defenses, frameworks, applications, and future directions AU - Daniel M. Jimenez-Gutierrez AU - Yelizaveta Falkouskaya AU - José L. Hernandez-Ramos AU - Aris Anagnostopoulos AU - Ioannis Chatzigiannakis AU - Andrea Vitaletti PY - 2026 JO - Information Fusion DO - 10.1016/j.inffus.2026.104155 UR - https://doi.org/10.1016/j.inffus.2026.104155 ER -
APA
Jimenez-Gutierrez, D. M., Falkouskaya, Y., Hernandez-Ramos, J. L., Anagnostopoulos, A., Chatzigiannakis, I., & Vitaletti, A. (2026). On the security and privacy of federated learning: A survey with attacks, defenses, frameworks, applications, and future directions. Information Fusion. https://doi.org/10.1016/j.inffus.2026.104155
Source records
- crossref · retrieved 2026-09-26T16:24:31.876Z