Balancing privacy and model performance in federated learning through contract-based data trading

Abstract The rise of the Internet of Things (IoT) has led to a huge amount of data beginning to emerge. Federated learning (FL) has received widespread attention and application as a new paradigm for data collection. However, data trading poses a threat to the privacy of data owners, and even participants in federated learning face the risk of data breaches. While many encryption methods have been proposed to mitigate these risks, the encrypted data negatively impacts the quality of the global model in federated learning. To this end, we propose an algorithm based on contract mechanisms to resolve the conflict between the privacy protection level of clients and the aggregation error on the federated learning server. Clients upload perturbed data according to their privacy protection levels, while mitigating the conflict between client data privacy protection and platform global model aggregation error. Through theoretical analysis and extensive experiments, our proposed trading method achieves desirable data utility while ensuring budget feasibility, individual rationality, and incentive compatibility.