Proof of Intelligence
Last updated
Last updated
The goal of Proof of Intelligence (PoI) is to provide ChainOpera L1 blockchain with a consensus algorithm for 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 workflow, federated learning, and beyond. For example, PoI should have such features:
Proof-of-contribution based reward allocation so that the AI contributors are compensated based on their contributions to the end outcome for AI (trained model, inference service, agent service, etc.)
Privacy-preserving decentralized model training or inference by avoiding any data movement from data owners;
Robustness against malicious parties (e.g., trainers aiming to poison the model);
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;
The following paper showcases the proof of contribution for collaborative machine learning on Blockchain. We will keep improving the PoI consensus algorithm along the way.
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.
