Budget-Feasible Double Auction Mechanisms for Model Training Services in Federated Learning Market

Ting Zhou, Hongtao Lv, Ning Liu, Lei Liu

Abstract: In cross-silo Federated Learning (FL), where data is distributed across multiple organizations such as hospitals, incentivizing collaboration while preserving data privacy presents a significant challenge. FL markets facilitate transactions among these organizations, introducing a budget-feasible two-sided allocation problem. Budget constraints are a realistic and crucial issue that reflects the economic capacities and engagement levels of market participants, but they also increase the complexity of allocation. To address this challenge, this paper proposes BudoMech, a novel budget-feasible double auction mechanism. BudoMech integrates requesters’ values and providers’ costs to determine allocations and payments, ensuring adherence to budget constraints while guaranteeing truthfulness. The mechanism sorts values and costs, uses virtual prices to iteratively calculate allocation quantities and prices, and then outputs the final allocations and payments. Theoretical analysis confirms that BudoMech satisfies desirable properties, including budget feasibility, individual rationality, truthfulness, and efficiency. Furthermore, extensive experiments demonstrate that BudoMech is an effective solution for resource allocation in FL markets.

External IDs:dblp:conf/trustcom/ZhouL0L24