Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction

Fuente: arXiv
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Main Authors: Witt, Leon, Toyoda, Kentaroh, Samek, Wojciech, Li, Dan
Format: Preprint
Published: 2026
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author Witt, Leon
Toyoda, Kentaroh
Samek, Wojciech
Li, Dan
author_facet Witt, Leon
Toyoda, Kentaroh
Samek, Wojciech
Li, Dan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_28434
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction
Witt, Leon
Toyoda, Kentaroh
Samek, Wojciech
Li, Dan
Cryptography and Security
Computers and Society
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.
title Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction
topic Cryptography and Security
Computers and Society
url https://arxiv.org/abs/2603.28434