Federated Learning by nature is susceptible to low-quality, corrupted, or
even malicious data that can severely degrade the quality of the learned model.
Traditional techniques for data valuation cannot be applied as the data is
never revealed. We present a novel technique for filtering, and scoring data
based on a practical influence approximation (`lazy' influence) that can be
implemented in a privacy-preserving manner. Each participant uses his own data
to evaluate the influence of another participant's batch, and reports to the
center an obfuscated score using differential privacy. Our technique allows for
highly effective filtering of corrupted data in a variety of applications.
Importantly, we show that most of the corrupted data can be filtered out
(recall of $>90\%$, and even up to $100\%$), even under really strong privacy
guarantees ($\varepsilon \leq 1$).

本文介绍了一种基于实际影响近似（lazy influence）的数据过滤和评分技术，该技术可以实现隐私保护，并允许在许多应用中高效地过滤损坏数据，其精度高，召回率达到 90% 以上，即使在强隐私保证（ϵ≤1）的情况下，也能将损坏的数据过滤掉。