Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions.

However,in manyapplications there are several different parameters one might wish to vary: for example, scale and density. In contrast to the one-parameter setting, techniques for applying statistics and machine learning in the setting of multiparameter persistence are not well understood due to the lack of a concise representationoftheresults.

Collaborative inference leverages diverse features provided by different agents (e.g.,sensors)formoreaccurateinference. Acommonsetupiswhereeachagent sends its embedded features instead of the raw data to the Fusion Center (FC) for joint prediction. In this setting, we consider inference phase attacks when asmall fraction of agents is compromised.