Federated learning enables machine algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated requires ensuring that agents (e.g., mobile devices) faithfully execute intended algorithm, which has been largely overlooked in literature. In this study, we first use risk bounds analyze how key feature learning, unbalan...