In this talk, I will describe an initial implementation of an accountable federated learning system that is privacy-preserving. BlockFLow incorporates differential privacy to reduce information leakage, introduces a novel auditing mechanism for evaluating model contribution, and uses Ethereum smart contracts to incentivize good behavior. Its primary goal is to reward agents proportional to the quality of their contribution while protecting the privacy of the underlying datasets and being resilient to malicious adversaries.