  Sparse or $\ell_0$ adversarial attacks arbitrarily perturb an unknown subset
of the features. $\ell_0$ robustness analysis is particularly well-suited for
heterogeneous (tabular) data where features have different types or scales.
State-of-the-art $\ell_0$ certified defenses are based on randomized smoothing
and apply to evasion attacks only. This paper proposes feature partition
aggregation (FPA) -- a certified defense against the union of $\ell_0$ evasion,
backdoor, and poisoning attacks. FPA generates its stronger robustness
guarantees via an ensemble whose submodels are trained on disjoint feature
sets. Compared to state-of-the-art $\ell_0$ defenses, FPA is up to
3,000${\times}$ faster and provides larger median robustness guarantees (e.g.,
median certificates of 13 pixels over 10 for CIFAR10, 12 pixels over 10 for
MNIST, 4 features over 1 for Weather, and 3 features over 1 for Ames), meaning
FPA provides the additional dimensions of robustness essentially for free.
