Abstract Decentralized Federated Learning (FL) offers a paradigm where independent
entities collaboratively train a machine learning model while preserving the privacy of their
datasets. Integrating blockchain technology into decentralized FL frameworks is critical to
establishing the trust necessary for user participation. However, existing FL systems using
blockchain often struggle with scalability, latency, and privacy issues, particularly in
permissionless blockchain contexts. This paper proposes OpenFL, a novel, collateral …