Title: Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph,
Abstract: We study the decentralized machine learning scenario where many users
collaborate to learn personalized models based on (i) their local datasets and
(ii) a similarity graph over the users' learning tasks. Our approach trains
nonlinear classifiers in a multi-task boosting manner without exchanging
personal data and with low communication costs. When background knowledge about
task similarities is not available, we propose to jointly learn the
personalized models and a sparse collaboration graph through an alternating
optimization procedure. We analyze the convergence rate, memory consumption and
communication complexity of our decentralized algorithms, and demonstrate the
benefits of our approach compared to competing techniques on synthetic and real
datasets.