A decision-theoretic model for a principal-agent collaborative learning problem

TL;DR

This paper introduces a decision-theoretic model for a collaborative learning setting where a principal dynamically assigns aggregation weights to agents, guiding them to reach an optimal consensus estimate through a feedback-driven, stable process.

Contribution

It develops a novel decision-theoretic framework for principal-agent collaborative learning with dynamic weight assignment and analyzes its stability and generalization benefits.

Findings

Framework enables stable consensus among agents.

Agents improve generalization without knowing dataset qualities.

Dynamic weights adapt to agent performance over time.

Abstract

In this technical note, we consider a collaborative learning framework with principal-agent setting, in which the principal at each time-step determines a set of appropriate aggregation coefficients based on how the current parameter estimates from a group of $K$ agents effectively performed in connection with a separate test dataset, which is not part of the agents' training model datasets. Whereas, the agents, who act together as a team, then update their parameter estimates using a discrete-time version of Langevin dynamics with mean-field-like interaction term, but guided by their respective different training model datasets. Here, we propose a decision-theoretic framework that explicitly describes how the principal progressively determines a set of nonnegative and sum to one aggregation coefficients used by the agents in their mean-field-like interaction term, that eventually leading them to reach a consensus optimal parameter estimate. Interestingly, due to the inherent feedbacks and cooperative behavior among the agents, the proposed framework offers some advantages in terms of stability and generalization, despite that both the principal and the agents do not necessarily need to have any knowledge of the sample distributions or the quality of each others' datasets.