Gossip Learning with Linear Models on Fully Distributed Data

TL;DR

This paper introduces gossip learning with linear models for fully distributed data, enabling privacy-preserving, low-communication classification through ensemble methods and theoretical convergence guarantees.

Contribution

It presents a novel gossip learning approach with ensemble-based model combination, achieving efficient, reliable distributed classification without raw data sharing.

Findings

The method converges theoretically.

It performs well on benchmark datasets.

It demonstrates robustness in distributed settings.

Abstract

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications. In this model we have one data record at each network node, but without the possibility to move raw data due to privacy considerations. For example, user profiles, ratings, history, or sensor readings can represent this case. This problem is difficult, because there is no possibility to learn local models, the system model offers almost no guarantees for reliability, yet the communication cost needs to be kept low. Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods. We present an instantiation of this approach for the case of classification with linear models. Our main contribution is an ensemble learning method which---through the continuous combination of the models in the network---implements a virtual weighted voting mechanism over an exponential number of models at practically no extra cost as compared to independent random walks. We prove the convergence of the method theoretically, and perform extensive experiments on benchmark datasets. Our experimental analysis demonstrates the performance and robustness of the proposed approach.