  We consider the problem of learning a binary classifier from $n$ different
data sources, among which at most an $\eta$ fraction are adversarial. The
overhead is defined as the ratio between the sample complexity of learning in
this setting and that of learning the same hypothesis class on a single data
distribution. We present an algorithm that achieves an $O(\eta n + \ln n)$
overhead, which is proved to be worst-case optimal. We also discuss the
potential challenges to the design of a computationally efficient learning
algorithm with a small overhead.
