Decentralized learning describes machine learning processes where data and model training occur across multiple independent nodes or devices. This approach contrasts with traditional centralized methods by distributing computational tasks and avoiding the aggregation of raw data in a single location. It often involves techniques like federated learning, where models are trained locally and only aggregated updates are shared. This method enhances data privacy and reduces reliance on central servers, aligning with blockchain principles.
Context
The integration of decentralized learning with blockchain technology is gaining traction, particularly for applications requiring secure, privacy-preserving data analysis without a central authority. Current research addresses challenges such as incentive mechanisms for participants, ensuring data integrity, and achieving consensus on model updates in a distributed environment. Future developments may include verifiable on-chain model training and secure data marketplaces.
A reinforcement learning engine enables BFT protocols to dynamically self-optimize, boosting throughput and establishing the first Learned Consensus paradigm.
The Proof of Inference Model (PoIm) enables cost-effective, on-chain machine learning inference to function as a real-time transaction firewall, mitigating billions in DeFi exploits.
Research introduces ZKPoT consensus, leveraging zk-SNARKs to cryptographically verify machine learning contributions without exposing private training data or model parameters.
SecureVFL integrates a permissioned blockchain, a novel Proof of Feature Sharing consensus, and Replicated Secret Sharing for private, verifiable multi-party federated learning.
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