DEVA: Decentralized, Verifiable Secure Aggregation for Privacy-Preserving Learning

DEVA proposes a decentralized secure aggregation protocol that is both privacy-preserving and verifiable.

Key features:

• Ensures correctness of the aggregation result through non-interactive verification
• Protects individual inputs in federated learning settings
• Tolerates malicious users and decentralized trust assumptions

The paper demonstrates that DEVA achieves strong security guarantees without central authorities.