Additive-Effect Assisted Learning

TL;DR

This paper introduces a privacy-preserving, two-stage collaborative learning framework enabling agents with different datasets to enhance modeling performance with limited communication, achieving near-centralized results.

Contribution

It proposes a novel two-stage assisted learning architecture that maintains data privacy and minimizes communication, with theoretical guarantees of optimal performance.

Findings

Achieves oracle performance with limited data transmission.

Develops a privacy-aware hypothesis testing screening method.

Demonstrates effectiveness through numerical experiments.

Abstract

It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modeling performance. We consider a scenario where different learners hold datasets with potentially distinct variables, and their observations can be aligned by a nonprivate identifier. Their collaboration faces the following difficulties: First, learners may need to keep data values or even variable names undisclosed due to, e.g., commercial interest or privacy regulations; second, there are restrictions on the number of transmission rounds between them due to e.g., communication costs. To address these challenges, we develop a two-stage assisted learning architecture for an agent, Alice, to seek assistance from another agent, Bob. In the first stage, we propose a privacy-aware hypothesis testing-based screening method for Alice to decide on the usefulness of the data from Bob, in a way that only requires Bob to transmit sketchy data. Once Alice recognizes Bob's usefulness, Alice and Bob move to the second stage, where they jointly apply a synergistic iterative model training procedure. With limited transmissions of summary statistics, we show that Alice can achieve the oracle performance as if the training were from centralized data, both theoretically and numerically.