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.

翻译：联邦学习（FL）因其利用本地分布式数据进行模型训练的高效性而日益受到认可。然而，在这一协作过程中，共享数据的合理估值问题仍未得到充分解决。本文将联邦学习构建为一个模型市场，其中客户端同时扮演买方和卖方角色，参与模型交易。该联邦学习市场允许客户端通过出售自身模型获得货币收益，并通过购买他人模型提升本地模型性能。我们提出一种基于拍卖的解决方案，以确保基于性能增益的合理定价。设计激励机制以鼓励客户端真实披露其模型估值。此外，我们引入强化学习（RL）框架用于市场运营，旨在动态变化的市场状态下实现最大交易量。在四个数据集上的实验结果表明，所提出的联邦学习市场能够实现高交易收益，并保证公平的下游任务准确性。