  Momentum is a variant of gradient descent that has been proposed for its
benefits on convergence. In a distributed setting, momentum can be implemented
either at the server or the worker side. When the aggregation rule used by the
server is linear, commutativity with addition makes both deployments
equivalent. Robustness and privacy are however among motivations to abandon
linear aggregation rules. In this work, we demonstrate the benefits on
robustness of using momentum at the worker side. We first prove that computing
momentum at the workers reduces the variance-norm ratio of the gradient
estimation at the server, strengthening Byzantine resilient aggregation rules.
We then provide an extensive experimental demonstration of the robustness
effect of worker-side momentum on distributed SGD.
