Proof of Intelligence
The goal of Proof of Intelligence (PoI) is to provide the ChainOpera L1 blockchain with a consensus algorithm tailored to the CoAI protocol. This algorithm coordinates AI resource providers (contributors) and developers across all AI foundational services—including training data management, model training, model serving, AI agent workflows, federated learning, and more.
PoI is designed with the following features:
Proof-of-contribution based allocation: AI contributors’ participation is measured and recorded according to their contributions (e.g., training, inference, or agent services). Allocation reflects the proportion of work provided within the ecosystem.
Privacy-preserving collaboration: Model training and inference can occur without moving raw data from data owners, using secure and privacy-preserving computation methods.
Robustness: The system is resistant to malicious behavior such as data poisoning or model tampering, ensuring trustworthy outcomes.
Verifiability: All computations in the protocol—including contribution assessments and outlier detection—are verifiable, ensuring correctness and transparency.
The following research paper demonstrates early designs for proof-of-contribution in collaborative machine learning on blockchain. PoI will continue to evolve as the protocol develops, strengthening consensus and reliability across the ecosystem.
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e.g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i.e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design. We propose a blockchain-based marketplace design to achieve all five objectives mentioned above. In our design, we utilize a distributed storage infrastructure and an aggregator aside from the project owner and the trainers. The aggregator is a processing node that performs certain computations, including assessing trainer contributions, removing outliers, and updating hyper-parameters. We execute the proposed data market through a blockchain smart contract. The deployed smart contract ensures that the project owner cannot evade payment, and honest trainers are rewarded based on their contributions at the end of training. Finally, we implement the building blocks of the proposed data market and demonstrate their applicability in practical scenarios through extensive experiments.
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