Federated Learning Incentive Mechanism under Buyers' Auction Market
Jiaxi Yang Zihao Guo Sheng Cao Cuifang Zhao Li-Chuan Tsai
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.