Personalized Federated Learning with Auction-Based Client Selection and Edge-Enhanced Model Accuracy

This work explores a Personalized Federated Learning (PFL) system with a central server, multiple edges and clients. Edges contain significant data distribution diversity and data scarcity. We design an auction-based online algorithm for dynamically selecting clients, who are motivated to connect with edges for model serving and participate in training for rewards. Our auction focuses on bids for model service usage, yet incorporates a reward mechanism to compute social welfare. This approach fosters enhanced collaboration between clients and edges. Through extensive simulation, we demonstrate that our algorithm substantially improves the usage of model serving requests from clients, showing an increase in social welfare with a low competitive ratio. The personalized models benefit from clients’ computational contributions and diverse datasets, while outperforming conventional FL frameworks in model accuracy. Our contributions offer a scalable, practical solution to PFL challenges, ensuring improved model performance and client engagement in a data-sensitive, reward-driven context.