Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction

Computer Science > Cryptography and Security [Submitted on 30 Mar 2026] Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction Leon Witt, Kentaroh Toyoda, Wojciech Samek, Dan Li 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. Comments: Published at the IEEE Conference on Artificial Intelligence 2024 in Singapore (Blockchain Workshop) Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY) Cite as: arXiv:2603.28434 [cs.CR]   (or arXiv:2603.28434v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.28434 Focus to learn more Submission history From: Leon Witt [view email] [v1] Mon, 30 Mar 2026 13:42:56 UTC (972 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)