Title: Personalized and Private Peer-to-Peer Machine Learning,
Abstract: The rise of connected personal devices together with privacy concerns call
for machine learning algorithms capable of leveraging the data of a large
number of agents to learn personalized models under strong privacy
requirements. In this paper, we introduce an efficient algorithm to address the
above problem in a fully decentralized (peer-to-peer) and asynchronous fashion,
with provable convergence rate. We show how to make the algorithm
differentially private to protect against the disclosure of information about
the personal datasets, and formally analyze the trade-off between utility and
privacy. Our experiments show that our approach dramatically outperforms
previous work in the non-private case, and that under privacy constraints, we
can significantly improve over models learned in isolation.