Robust Aggregation
A core technique in federated learning for defending against Byzantine faults. It replaces arithmetic averaging with methods such as coordinate-wise median, trimmed mean, and Krum to identify and suppress anomalous data uploaded by malicious nodes.
Timeline (last 90 days)
The algorithm replaces naive averaging with robust aggregation at the central server to identify and suppress anomalous uploads that deviate from the normal distribution.
Common robust aggregation methods include coordinate-wise median, trimmed mean, Krum, and its variants.
Theoretical guarantees for robust aggregation typically rely on the assumption that the proportion of malicious nodes is below a certain threshold (e.g., no more than half of the total nodes).
All Facts (3)
The algorithm replaces naive averaging with robust aggregation at the central server to identify and suppress anomalous uploads that deviate from the normal distribution.
50%UnverifiedCommon robust aggregation methods include coordinate-wise median, trimmed mean, Krum, and its variants.
50%UnverifiedTheoretical guarantees for robust aggregation typically rely on the assumption that the proportion of malicious nodes is below a certain threshold (e.g., no more than half of the total nodes).
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