A VCG-based Fair Incentive Mechanism for Federated Learning

TL;DR

This paper introduces a novel VCG-based incentive mechanism called PVCG for federated learning, ensuring truthfulness, efficiency, and rationality, with a new payment computation method suitable for noisy environments.

Contribution

It develops the PVCG sharing rule for CVGP games, proving its desirable properties, and proposes the RIM method for payment calculation in noisy settings.

Findings

PVCG achieves truthfulness, Pareto efficiency, individual rationality, and weak budget balance.

RIM effectively computes payments and procurement levels in noisy environments.

The mechanism supports Pareto-efficient digital production in federated learning contexts.

Abstract

The enduring value of the Vickrey-Clarke-Groves (VCG) mechanism has been highlighted due to its adoption by Facebook ad auctions. Our research delves into its utility in the collaborative virtual goods production (CVGP) game, which finds application in realms like federated learning and crowdsourcing, in which bidders take on the roles of suppliers rather than consumers. We introduce the Procurement-VCG (PVCG) sharing rule into existing VCG mechanisms such that they can handle capacity limits and the continuous strategy space characteristic of the reverse auction setting in CVGP games. Our main theoretical contribution provides mathematical proofs to show that PVCG is the first in the CVGP game context to simultaneously achieve truthfulness, Pareto efficiency, individual rationality, and weak budget balance. These properties suggest the potential for Pareto-efficient production in the digital planned economy. Moreover, to compute the PVCG payments in a noisy economic environment, we propose the Report-Interpolation-Maximization (RIM) method. RIM facilitates the learning of the optimal procurement level and PVCG payments through iterative interactions with suppliers.