Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction

2026-03-30

AI Summary

This work addresses the challenges of integrating federated learning with blockchain, particularly the high computational overhead of contribution evaluation and the difficulty of aligning such mechanisms with on-chain resource constraints. To overcome these limitations, the authors propose a lightweight framework that embeds a multi-task peer prediction mechanism within blockchain smart contracts. This approach enables dynamic assessment of participants’ data and computational contributions at low computational cost, facilitating fair cryptocurrency-based incentive allocation. By design, the method preserves decentralization and incentive compatibility while substantially reducing on-chain computational burden. The resulting scalable federated learning framework is rigorously analyzed to elucidate its advantages and inherent limitations.

Abstract

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.

Problem

• Federated Learning
• Blockchain
• Contribution Measurement
• Decentralization
• Computation Constraints

Innovation

• Federated Learning
• Blockchain
• Multi-Task Peer Prediction
• Smart Contracts
• Incentive Mechanism