DataFed: Bid-Agnostic Auctions for Hierarchical Client Selection in Online Federated Edge Learning

Dynamic client availability and resource variations motivate online federated edge learning (OFEL), where real-time client selection requires truthful auctions to align clients’ bids with their actual costs. However, existing auctions face three key challenges: (i) two-sided incomplete information in bid-agnostic settings, (ii) hierarchical objective misalignment between cloud and edge servers, and (iii) lack of long-term optimization in dynamic environments. To address these challenges, we propose DataFed, a bid-agnostic auction mechanism that integrates dynamic Bayesian bid distribution estimation with Stackelberg-based client selection. Specifically, we formulate hierarchical client selection as a dynamic Bayesian Stackelberg game with a multi-armed bandit objective to minimize cumulative expected regret. With a known bid distribution, we establish a Stackelberg equilibrium for the normalized proxy model. When the distribution is unknown but fixed, we derive estimation error bounds under independent proxy feedback. With sufficient exploration, we prove sublinear cumulative expected regret relative to the proxy benchmark. By adaptively selecting clients with favorable utility-cost trade-offs, DataFed improves learning efficiency and stabilizes online training in dynamic environments. Experiments show that DataFed reduces mean cumulative regret by 23.0% on average across four real-world datasets and improves mean cloud utility by 7.9% through ES response anticipation.