Scalable Privacy-Preserving Federated Machine Learning
Summary
Scalable Privacy-Preserving Federated Machine Learning Analytics. Using multi-party homomorphic encryption (MHE) and federated learning for data confidentiality. Supports GLMs, MLPs, CNNs, RNNs, and PCA. Workflow-specific optimizations for scalability.
Themes
Keywords
federated learning, homomorphic encryption, privacy-preserving ML, MHE, scalability
Poster
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