Optimal Communication-Computation Trade-Off in Heterogeneous Gradient Coding

Gradient coding allows a master node to derive the aggregate of the partial gradients, calculated by some worker nodes over the local data sets, with minimum communication cost, and in the presence of stragglers. In this paper, for gradient coding with linear encoding, we characterize the optimum communication cost for heterogeneous distributed systems with
arbitrary
data placement, with
$s \in \mathbb {N}$
stragglers and
$a \in \mathbb {N}$
adversarial nodes. In particular, we show that the optimum communication cost, normalized by the size of the gradient vectors, is equal to
$(r-s-2a)^{-1}$
, where
$r \in \mathbb {N}$
is the minimum number that a data partition is replicated. In other words, the communication cost is determined by the data partition with the minimum replication, irrespective of the structure of the placement. The proposed achievable scheme also allows us to target the computation of a polynomial function of the aggregated gradient matrix. It also allows us to borrow some ideas from approximation computing and propose an approximate gradient coding scheme for the cases when the repetition in data placement is smaller than what is needed to meet the restriction imposed on communication cost or when the number of stragglers appears to be more than the presumed value in the system design.