Why FHE in Federated Learning?
1. Enhanced Privacy:
FL already ensures data stays on the client side, but transmitting gradients or model updates to a central server can still leak sensitive information through inference attacks.
FHE adds an extra layer of security by ensuring that even the server cannot access the raw gradients or updates—it only processes encrypted data.
Homomorphic encryption is a cutting-edge cryptographic technique that allows computations on encrypted data without decryption, ensuring the underlying information’s confidentiality.
This encryption method is classified into three main categories: Partial Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE).
Artificial intelligence (AI) is transforming various industries and enhancing the way we live and work. However, its rapid evolution brings the responsibility to ensure that AI systems are developed securely and ethically.
Below, we outline essential guidelines for providers creating AI systems, whether developing them from scratch or utilizing pre-existing tools and services.
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NVIDIA Inception is a program designed to help startups evolve faster through cutting-edge technology, opportunities to connect with venture capitalists, and access to the latest technical resources from NVIDIA.
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