PySyft: A Library for Easy Federated Learning
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PySyft is a flexible, open-source framework for encrypted, privacy-preserving machine learning that extends PyTorch and TensorFlow to enable multi-party computations and HE.
“PySyft [146] is a flexible, open-source framework for encrypted, privacy-preserving machine learning.PySyft extends PyTorch and TensorFlow to enable multi-party computations (MPC) and HE.”
From Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study (2025) · cites this paper as [146]
Cites
Secure, privacy-preserving and federated machine learning in medical imaging
Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,501 · see citing works
Self-citationPrivacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991401 · Cited by 712 · see citing works
Flower: A Friendly Federated Learning Research Framework
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Li, K. H., Parcollet, T., de Gusmão, P. P. B., et al. (2020) · arXiv · doi:10.48550/arxiv.2007.14390 · Cited by 622 · see citing works
Rodríguez-Barroso, N., Stipcich, G., Jiménez-López, D., Ruiz-Millán, J. A., Martínez-Cámara, E., González-Seco, G., Luzón, M. V., Veganzones, M. A., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.009 · Cited by 148 · see citing works
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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625 · see citing works
“Other frameworks, such as PySyft [144], provide interfaces for developers to implement their training algorithm.”
MethodPrivacy-preserving artificial intelligence in healthcare: Techniques and applications
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106848 · Cited by 581 · see citing works
Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228 · see citing works
Kalapaaking, A. P., Khalil, I., & Yi, X. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3268186 · Cited by 122 · see citing works
Exploring privacy measurement in federated learning
Jagarlamudi, G. K., Yazdinejad, A., Parizi, R. M., & Pouriyeh, S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-023-05846-4 · Cited by 52 · see citing works
Rachakonda, S., Moorthy, S., Jain, A., Bukharev, A., Bucur, A., Manni, F., Quiterio, T. M., Joosten, L., & Mendez, N. I. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185418 · Cited by 39 · see citing works
Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study
Zobaed, S., & Amini Salehi, M. (2025) · Software Practice and Experience · doi:10.1002/spe.3398 · Cited by 21 · see citing works
- Authors
- Ziller, A., Trask, A., Lopardo, A., Szymkow, B., Wagner, B., Bluemke, E., Nounahon, J.-M., Passerat-Palmbach, J., Prakash, K., Rose, N., Ryffel, T., Reza, Z. N., & Kaissis, G.
- Affiliations
- Technical University of Munich (DE)
- University of Oxford (GB)
- ETH Zurich (CH)
- International Institute of Information Technology, Hyderabad (IN)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Thales (Canada) (CA)
- Imperial College London (GB)
- Corresponding author
- Kaissis, Georgios
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 111-139
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W3172018708
- Cited by
- 246
- References
- 4
- Citations by year
- 2026: 28 · 2025: 65 · 2024: 62 · 2023: 52 · 2022: 33 · 2021: 6
- FWCI
- 63.95
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Studies in computational intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, privacy-preserving machine learning, multi-party computation, differential privacy, PyTorch, Python interface, convolutional neural network training, duets, open-source library
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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.
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