  Stragglers, Byzantine workers, and data privacy are the main bottlenecks in
distributed cloud computing. Some prior works proposed coded computing
strategies to jointly address all three challenges. They require either a large
number of workers, a significant communication cost or a significant
computational complexity to tolerate Byzantine workers. Much of the overhead in
prior schemes comes from the fact that they tightly couple coding for all three
problems into a single framework. In this paper, we propose Adaptive Verifiable
Coded Computing (AVCC) framework that decouples the Byzantine node detection
challenge from the straggler tolerance. AVCC leverages coded computing just for
handling stragglers and privacy, and then uses an orthogonal approach that
leverages verifiable computing to mitigate Byzantine workers. Furthermore, AVCC
dynamically adapts its coding scheme to trade-off straggler tolerance with
Byzantine protection. We evaluate AVCC on a compute-intensive distributed
logistic regression application. Our experiments show that AVCC achieves up to
$4.2\times$ speedup and up to $5.1\%$ accuracy improvement over the
state-of-the-art Lagrange coded computing approach (LCC). AVCC also speeds up
the conventional uncoded implementation of distributed logistic regression by
up to $7.6\times$, and improves the test accuracy by up to $12.1\%$.
