Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction
This work addresses the problem of efficient contribution measurement in federated learning for decentralized AI systems, though it appears incremental as it builds on existing synergies between federated learning and blockchain.
The paper tackles the challenge of measuring contributions in blockchain-based federated learning due to computational constraints, proposing a decentralized AI training approach using blockchain and multi-task peer prediction to incentivize participation.
The synergy between Federated Learning and blockchain has been considered promising; however, the computationally intensive nature of contribution measurement conflicts with the strict computation and storage limits of blockchain systems. We propose a novel concept to decentralize the AI training process using blockchain technology and Multi-task Peer Prediction. By leveraging smart contracts and cryptocurrencies to incentivize contributions to the training process, we aim to harness the mutual benefits of AI and blockchain. We discuss the advantages and limitations of our design.