An Auction-based Marketplace for Model Trading in Federated Learning
This addresses the challenge of incentivizing data sharing in federated learning for clients, though it is incremental as it builds on existing FL frameworks with a market-based approach.
The paper tackles the problem of properly valuing shared data in federated learning by framing it as a marketplace where clients trade models, using an auction-based solution and reinforcement learning to maximize trading volumes and ensure fair pricing. Experimental results on four datasets show it achieves high trading revenue and fair downstream task accuracy.
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.