Fault-tolerant distributed logistic regression using unreliable components

Abstract

We consider the problem of computing distributed logistic regression using unreliable components. We consider both faults in the memory units and faults in the processing units. We show that using a real-number-coding technique, we can suppress errors during the computation and ensure that logistic regression converges with bounded error if the number of faults that happen during each iteration of the logistic regression is bounded, even when the faults happen in an adversarial manner. Moreover, since the coding technique is based on computation with real numbers, we show that the error-correction can be carried out at the algorithmic level (or block-level) based on the results from intermediate steps of logistic regression. Therefore, we only need to add redundant hardware at block-level, not the circuit level, for achieving fault-tolerance in the computation of logistic regression.