ASCII: ASsisted Classification with Ignorance Interchange

TL;DR

ASCII is a decentralized, privacy-aware classification method where agents iteratively exchange ignorance values to improve performance without sharing raw data, suitable for diverse classifiers and scenarios.

Contribution

The paper introduces ASCII, a novel iterative ignorance exchange approach enabling agents to enhance classification accuracy collaboratively while preserving privacy.

Findings

Effective across various classifier types.

Suitable for privacy-sensitive, decentralized environments.

Demonstrated improved performance in extensive experiments.

Abstract

The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular population of subjects. While the predictive performance of an agent may be enhanced by transmitting other data to it, this is often unrealistic due to intractable transmission costs and security concerns. While the predictive performance of an agent may be enhanced by transmitting other data to it, this is often unrealistic due to intractable transmission costs and security concerns. In this paper, we propose a method named ASCII for an agent to improve its classification performance through assistance from other agents. The main idea is to iteratively interchange an ignorance value between 0 and 1 for each collated sample among agents, where the value represents the urgency of further assistance needed. The method is naturally suitable for privacy-aware, transmission-economical, and decentralized learning scenarios. The method is also general as it allows the agents to use arbitrary classifiers such as logistic regression, ensemble tree, and neural network, and they may be heterogeneous among agents. We demonstrate the proposed method with extensive experimental studies.