Privacy-preserving Data Filtering in Federated Learning Using Influence Approximation. (arXiv:2205.11518v1 [cs.CR])

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 that can be implemented in a
privacy-preserving manner. Each agent uses his own data to evaluate the
influence of another agent's batch, and reports to the center …

approximation arxiv data federated learning filtering influence learning privacy