Collaborative training refers to a method where multiple entities work together to develop or refine artificial intelligence models. This process involves sharing data, computational resources, or model parameters to achieve improved performance or efficiency. In the context of blockchain, it can involve decentralized networks contributing to machine learning tasks while maintaining data privacy through techniques like federated learning. This approach can yield more robust and generalized AI systems.
Context
The application of collaborative training in decentralized systems presents both opportunities and technical hurdles. It offers a path to leverage distributed computational power and diverse datasets without centralizing sensitive information, which aligns with blockchain’s privacy principles. Current discussions center on creating secure and efficient protocols for data exchange and model aggregation within permissioned or public blockchain environments. Its success could significantly accelerate AI development within Web3 applications.
This research introduces Zero-Knowledge Proof of Training, a zk-SNARK-based consensus mechanism that validates machine learning contributions without compromising participant data privacy, enabling secure, scalable decentralized AI.
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