Auction Based Federated Learning

Auction-based federated learning (AFL) uses economic incentives to motivate data owners to participate in federated learning, addressing the challenge of data scarcity and privacy concerns. Current research focuses on developing sophisticated bidding strategies for both data owners and consumers, often employing agent-based systems and reinforcement learning to optimize resource allocation and model accuracy. These advancements aim to improve the efficiency and fairness of federated learning, leading to more robust and generalizable models while ensuring data owners are fairly compensated for their contributions. The ultimate goal is to create a sustainable and scalable ecosystem for collaborative machine learning.