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Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction

Leon Witt, Kentaroh Toyoda, Wojciech Samek, Dan Li

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.

Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction

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.

Paper Structure

This paper contains 8 sections, 1 equation, 2 figures.

Table of Contents

  1. Introduction
  2. Contribution Measurement in FL
  3. Peer Prediction
  4. Application of Blockchain in Federated Learning
  5. Decentralized and Incentivized Federated Learning: A framework
  6. MTPP to incentivize FL on blockchain
  7. Limitations and Future research
  8. Conclusion

Figures (2)

  • Figure 1: FL vs. Decentralized and Incentivized FLF from LeonSP
  • Figure 2: Incentivized and Decentralized Federated Learning: Application of a Multi-task peer prediction mechanism to incentivize FL on Blockchain.