An Auction-based Marketplace for Model Trading in Federated Learning
Yue Cui Liuyi Yao Yaliang Li Ziqian Chen Bolin Ding Xiaofang Zhou
Federated learning (FL) is increasingly recognized for its efficacy in
training models using locally distributed data. However, the proper valuation
of shared data in this collaborative process remains insufficiently addressed.
In this work, we frame FL as a marketplace of models, where clients act as both
buyers and sellers, engaging in model trading. This FL market allows clients to
gain monetary reward by selling their own models and improve local model
performance through the purchase of others' models. We propose an auction-based
solution to ensure proper pricing based on performance gain. Incentive
mechanisms are designed to encourage clients to truthfully reveal their model
valuations. Furthermore, we introduce a reinforcement learning (RL) framework
for marketing operations, aiming to achieve maximum trading volumes under the
dynamic and evolving market status. Experimental results on four datasets
demonstrate that the proposed FL market can achieve high trading revenue and
fair downstream task accuracy.