  User active authentication on mobile devices aims to learn a model that can
correctly recognize the enrolled user based on device sensor information. Due
to lack of negative class data, it is often modeled as a one-class
classification problem. In practice, mobile devices are connected to a central
server, e.g, all android-based devices are connected to Google server through
internet. This device-server structure can be exploited by recently proposed
Federated Learning (FL) and Split Learning (SL) frameworks to perform
collaborative learning over the data distributed among multiple devices. Using
FL/SL frameworks, we can alleviate the lack of negative data problem by
training a user authentication model over multiple user data distributed across
devices. To this end, we propose a novel user active authentication training,
termed as Federated Active Authentication (FAA), that utilizes the principles
of FL/SL. We first show that existing FL/SL methods are suboptimal for FAA as
they rely on the data to be distributed homogeneously (i.e. IID) across
devices, which is not true in the case of FAA. Subsequently, we propose a novel
method that is able to tackle heterogeneous/non-IID distribution of data in
FAA. Specifically, we first extract feature statistics such as mean and
variance corresponding to data from each user which are later combined in a
central server to learn a multi-class classifier and sent back to the
individual devices. We conduct extensive experiments using three active
authentication benchmark datasets (MOBIO, UMDAA-01, UMDAA-02) and show that
such approach performs better than state-of-the-art one-class based FAA methods
and is also able to outperform traditional FL/SL methods.
