  How can we debug a logistical regression model in a federated learning
setting when seeing the model behave unexpectedly (e.g., the model rejects all
high-income customers' loan applications)? The SQL-based training data
debugging framework has proved effective to fix this kind of issue in a
non-federated learning setting. Given an unexpected query result over model
predictions, this framework automatically removes the label errors from
training data such that the unexpected behavior disappears in the retrained
model. In this paper, we enable this powerful framework for federated learning.
The key challenge is how to develop a security protocol for federated debugging
which is proved to be secure, efficient, and accurate. Achieving this goal
requires us to investigate how to seamlessly integrate the techniques from
multiple fields (Databases, Machine Learning, and Cybersecurity). We first
propose FedRain, which extends Rain, the state-of-the-art SQL-based training
data debugging framework, to our federated learning setting. We address several
technical challenges to make FedRain work and analyze its security guarantee
and time complexity. The analysis results show that FedRain falls short in
terms of both efficiency and security. To overcome these limitations, we
redesign our security protocol and propose Frog, a novel SQL-based training
data debugging framework tailored for federated learning. Our theoretical
analysis shows that Frog is more secure, more accurate, and more efficient than
FedRain. We conduct extensive experiments using several real-world datasets and
a case study. The experimental results are consistent with our theoretical
analysis and validate the effectiveness of Frog in practice.
