Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives

In recent years, research on the data trading market has been continuously deepened. In the transaction process, there is an information asymmetry process between agents and sellers. For sellers, direct data delivery faces the risk of privacy leakage. At the same time, sellers are not willing to provide data. A reasonable compensation method is needed to encourage sellers to provide data resources. For agents, the quality of data provided by sellers needs to be examined and evaluated. Otherwise, agents may consume too much cost and resources by recruiting sellers with poor data quality. Therefore, it is necessary to build a complete delivery process for the interaction between sellers and agents in the trading market so that the needs of sellers and agents can be met. The federated learning architecture is widely used in the data market due to its good privacy protection. Therefore, in this work, in response to the above challenges, we propose a transaction framework based on the federated learning architecture, and design a seller selection algorithm and incentive compensation mechanism. Specifically, we use gradient similarity and Shapley algorithm to fairly and accurately evaluate the contribution of sellers, and use the modified UCB algorithm to select sellers. After the training, fair compensation is made according to the seller's participation in the training. In view of the above work, we designed reasonable experiments for demonstration and obtained results, proving the rationality and effectiveness of the framework.

Data Trading Combination Auction Mechanism based on the Exponential Mechanism

With the widespread application of machine learning technology in recent years, the demand for training data has increased significantly, leading to the emergence of research areas such as data trading. The work in this field is still in the developmental stage. Different buyers have varying degrees of demand for various types of data, and auctions play a role in such scenarios due to their authenticity and fairness. Recent related work has proposed combination auction mechanisms for different domains. However, such mechanisms have not addressed the privacy concerns of buyers. In this paper, we design a \textit{Data Trading Combination Auction Mechanism based on the exponential mechanism} (DCAE) to protect buyers' bidding privacy from being leaked. We apply the exponential mechanism to select the final settlement price for the auction and generate a probability distribution based on the relationship between the price and the revenue. In the experimental aspect, we consider the selection of different mechanisms under two scenarios, and the experimental results show that this method can ensure high auction revenue and protect buyers' privacy from being violated.