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Category: Protocols & Standards
Category: Protocols & Standards
Secure Aggregation is a cryptographic protocol that enables combining model updates from multiple parties without revealing individual contributions, essential for privacy-preserving distributed AI.
The protocol uses cryptographic techniques like secret sharing and homomorphic encryption to aggregate gradients or parameters. Only the final sum is revealed, not individual updates.
Multi-party computation ensures no single party can access individual contributions, even if some participants are malicious.
Secure Aggregation enables collaborative AI training across organizations without sharing raw data. It's fundamental for federated learning in healthcare and finance.
The protocol makes privacy-preserving AI practical at scale, enabling new applications in sensitive domains.
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