Communication-efficient distributed eigenspace estimation with arbitrary node failures
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We develop an eigenspace estimation algorithm for distributed environments with arbitrary node failures, where a subset of computing nodes can return structurally valid but otherwise arbitrarily chosen responses. Notably, this setting encompasses several important scenarios that arise in distributed computing and data-collection environments such as silent/soft errors, outliers or corrupted data at certain nodes, and adversarial responses. Our estimator builds upon and matches the performance of a recently proposed non-robust estimator up to an additive Õ(σ√(α)) error, where σ^2 is the variance of the existing estimator and α is the fraction of corrupted nodes.
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