  We consider gradient coding in the presence of an adversary, controlling
so-called malicious workers trying to corrupt the computations. Previous works
propose the use of MDS codes to treat the inputs of the malicious workers as
errors and correct them using the error-correction properties of the code. This
comes at the expense of increasing the replication, i.e., the number of workers
each partial gradient is computed by. In this work, we reduce replication by
proposing a method that detects the erroneous inputs from the malicious
workers, hence transforming them into erasures. For $s$ malicious workers, our
solution can reduce the replication to $s+1$ instead of $2s+1$ for each partial
gradient at the expense of only $s$ additional computations at the main node
and additional rounds of light communication between the main node and the
workers. We give fundamental limits of the general framework for fractional
repetition data allocation. Our scheme is optimal in terms of replication and
local computation but incurs a communication cost that is asymptotically, in
the size of the dataset, a multiplicative factor away from the derived bound.
