Client-server multi-task learning from distributed datasets

TL;DR

This paper introduces a client-server framework for multi-task learning from distributed datasets that preserves data privacy while enabling shared learning across tasks using kernel methods.

Contribution

It proposes a novel algorithmic framework based on regularization and mixed effect kernels for multi-task learning in a privacy-preserving distributed setting.

Findings

Effective information fusion from distributed datasets.

Preserves privacy of individual data.

Demonstrated with a simulated music recommendation system.

Abstract

A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client is associated with an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multiple datasets while preserving privacy of individual data. The role of the server is to collect data in real-time from the clients and codify the information in a common database. The information coded in this database can be used by all the clients to solve their individual learning task, so that each client can exploit the informative content of all the datasets without actually having access to private data of others. The proposed algorithmic framework, based on regularization theory and kernel methods, uses a suitable class of mixed effect kernels. The new method is illustrated through a simulated music recommendation system.