Zero-Knowledge Proof of Training Secures Decentralized Learning Consensus and Privacy
ZKPoT is a new consensus primitive using zk-SNARKs to verify decentralized machine learning contribution without revealing sensitive model data, solving the privacy-efficiency trade-off.
Zero-Knowledge Proof of Training Secures Federated Learning Consensus
ZKPoT uses zk-SNARKs to verify model contributions privately, eliminating the trade-off between decentralized AI privacy and consensus efficiency.
Zero-Knowledge Proof of Training Secures Federated Consensus
The Zero-Knowledge Proof of Training consensus mechanism uses zk-SNARKs to prove model performance without revealing private data, solving the privacy-utility conflict in decentralized computation.
