Fedlabx: a practical and privacy-preserving framework for federated learning
Abstract Federated learning (FL) draws attention in academia and industry due to its privacy-preserving capability in training machine learning models. However, there are still some critical security attacks and vulnerabilities, including gradients leakage and interference attacks. Meanwhile, commun...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-07-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01184-3 |