  Federated learning enables the development of a machine learning model among
collaborating agents without requiring them to share their underlying data.
However, malicious agents who train on random data, or worse, on datasets with
the result classes inverted, can weaken the combined model. BlockFLow is an
accountable federated learning system that is fully decentralized and
privacy-preserving. 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. Specifically, BlockFLow
incorporates differential privacy, introduces a novel auditing mechanism for
model contribution, and uses Ethereum smart contracts to incentivize good
behavior. Unlike existing auditing and accountability methods for federated
learning systems, our system does not require a centralized test dataset,
sharing of datasets between the agents, or one or more trusted auditors; it is
fully decentralized and resilient up to a 50% collusion attack in a malicious
trust model. When run on the public Ethereum blockchain, BlockFLow uses the
results from the audit to reward parties with cryptocurrency based on the
quality of their contribution. We evaluated BlockFLow on two datasets that
offer classification tasks solvable via logistic regression models. Our results
show that the resultant auditing scores reflect the quality of the honest
agents' datasets. Moreover, the scores from dishonest agents are statistically
lower than those from the honest agents. These results, along with the
reasonable blockchain costs, demonstrate the effectiveness of BlockFLow as an
accountable federated learning system.
