  We tackle the problem of Byzantine errors in distributed gradient descent
within the Byzantine-resilient gradient coding framework. Our proposed solution
can recover the exact full gradient in the presence of $s$ malicious workers
with a data replication factor of only $s+1$. It generalizes previous solutions
to any data assignment scheme that has a regular replication over all data
samples. The scheme detects malicious workers through additional interactive
communication and a small number of local computations at the main node,
leveraging group-wise comparisons between workers with a provably optimal
grouping strategy. The scheme requires at most $s$ interactive rounds that
incur a total communication cost logarithmic in the number of data samples.
