Zero-Knowledge Proof of Training Secures Private Decentralized Machine Learning Consensus
ZKPoT introduces zk-SNARKs to consensus, enabling private validation of machine learning contributions to unlock scalable, trustless federated systems.
Inner-Product Argument Vector Commitments Enable Constant-Time Proof Aggregation
This new Inner-Product Argument Vector Commitment achieves constant-time state verification, fundamentally unlocking truly scalable stateless clients.
Zero-Knowledge Proof of Training Secures Decentralized AI Consensus
ZKPoT consensus leverages zk-SNARKs to cryptographically verify model performance in Federated Learning, eliminating privacy trade-offs and scaling decentralized AI.
Zero-Knowledge Proof of Training Secures Private Federated Consensus
ZKPoT consensus leverages zk-SNARKs to cryptographically validate a participant's model performance without revealing the underlying data or updates, unlocking scalable, private, on-chain AI.
Zero-Knowledge Proof of Training Secures Federated Consensus
Research introduces ZKPoT consensus, leveraging zk-SNARKs to cryptographically verify machine learning contributions without exposing private training data or model parameters.
Dagama Integrates Monad, Leads Galxe Starboard for Scalable Web3 Discovery
daGama's Monad integration and community traction redefine real-world discovery, establishing a new standard for scalable, user-centric dApps.
Dagama Tops Galxe Starboard, Integrates Monad for Scalable Discovery
daGama's community-driven ascent and Monad integration redefines scalable, incentivized real-world location discovery, enhancing Web3 social utility.
