Federated Learning Incentive Mechanism under Buyers' Auction Market
This addresses incentive mechanisms for self-interested participants in federated learning, but it is incremental as it adapts existing auction frameworks to a new market scenario.
The paper tackles the shift from a sellers' to a buyers' market in auction-based federated learning by adapting a procurement auction framework to explain pricing behavior, and it validates the approach with experimental results showing effectiveness.
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.