  Many current Internet services rely on inferences from models trained on user
data. Commonly, both the training and inference tasks are carried out using
cloud resources fed by personal data collected at scale from users. Holding and
using such large collections of personal data in the cloud creates privacy
risks to the data subjects, but is currently required for users to benefit from
such services. We explore how to provide for model training and inference in a
system where computation is pushed to the data in preference to moving data to
the cloud, obviating many current privacy risks. Specifically, we take an
initial model learnt from a small set of users and retrain it locally using
data from a single user. We evaluate on two tasks: one supervised learning
task, using a neural network to recognise users' current activity from
accelerometer traces; and one unsupervised learning task, identifying topics in
a large set of documents. In both cases the accuracy is improved. We also
analyse the robustness of our approach against adversarial attacks, as well as
its feasibility by presenting a performance evaluation on a representative
resource-constrained device (a Raspberry Pi).
