Federated Learning Incentive Mechanism under Buyers' Auction Market

TL;DR

This paper explores an auction-based federated learning incentive mechanism tailored for a buyers' market, incorporating blockchain for reputation management, and analyzes pricing strategies under different information scenarios.

Contribution

It introduces a procurement auction framework for federated learning in buyers' markets and integrates blockchain-based reputation systems to enhance client selection.

Findings

Effective pricing strategies under buyers' market conditions.

Blockchain reputation mechanism improves client reliability and security.

Validated approach through experimental results.

Abstract

Auction-based Federated Learning (AFL) enables open collaboration among self-interested data consumers and data owners. Existing AFL approaches are commonly under the assumption of sellers' market in that the service clients as sellers are treated as scarce resources so that the aggregation servers as buyers need to compete the bids. Yet, as the technology progresses, an increasing number of qualified clients are now capable of performing federated learning tasks, leading to shift from sellers' market to a buyers' market. In this paper, we shift the angle by adapting the procurement auction framework, aiming to explain the pricing behavior under buyers' market. Our modeling starts with basic setting under complete information, then move further to the scenario where sellers' information are not fully observable. In order to select clients with high reliability and data quality, and to prevent from external attacks, we utilize a blockchain-based reputation mechanism. The experimental results validate the effectiveness of our approach.