Zero-Knowledge Proof of Training Secures Decentralized Federated Learning Consensus
ZKPoT uses zk-SNARKs to verify decentralized model accuracy without revealing private data, solving the efficiency-privacy trade-off in federated learning.
Set Byzantine Consensus Decentralizes Rollup Sequencers and Data Availability
Set Byzantine Consensus introduces a decentralized "arranger" for rollups, fundamentally solving the single-node sequencer bottleneck and enhancing censorship resistance.
Zero-Knowledge Proof of Training Secures Decentralized AI Consensus
A new Zero-Knowledge Proof of Training (ZKPoT) consensus mechanism leverages zk-SNARKs to cryptographically verify model performance, eliminating Proof-of-Stake centralization and preserving data privacy in decentralized machine learning.
Compositional Formal Verification Secures Complex DAG Consensus Protocols
This framework modularizes DAG consensus proofs into reusable components, dramatically reducing verification effort and ensuring robust protocol safety.
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 Private Decentralized AI Consensus
ZKPoT, a novel zk-SNARK-based consensus, cryptographically validates decentralized AI model contributions, eliminating privacy risks and scaling 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.
QScale: Probabilistic Chained Consensus for Moderate-Scale Systems
QScale introduces a novel probabilistic chained consensus, significantly reducing communication overhead for distributed ledgers at moderate scales.
Aggregating Node Preferences Enhances Byzantine Fault Tolerance in Blockchain Consensus
A novel PBFT algorithm allows nodes to express preferences, integrating incentive mechanisms and verifiable randomness to achieve robust multi-value consensus.
