Auction-based Federated Learning (AFL) has emerged as an important research field in recent years. The prevailing strategies for FL model users (MUs) assume that the entire team of the required data owners (DOs) for an FL task must be assembled before training can commence. In practice, an MU can trigger the FL training process multiple times. DOs can thus be gradually recruited over multiple FL model training sessions. Existing bidding strategies for AFL MUs are not designed to handle such scenarios. Therefore, the problem of multi-session AFL remains open. To address this problem, we propose the Multi-session Budget Optimization Strategy for forward Auction-based Federated Learning (MultiBOS-AFL). Based on hierarchical reinforcement learning, MultiBOS-AFL jointly optimizes inter-session budget pacing and intra-session bidding for AFL MUs, with the objective of maximizing the total utility. Extensive experiments on six benchmark datasets show that it significantly outperforms seven state-of-the-art approaches. On average, MultiBOS-AFL achieves 12.28% higher utility, 14.52% more data acquired through auctions for a given budget, and 1.23% higher test accuracy achieved by the resulting FL model compared to the best baseline. To the best of our knowledge, it is the first budget optimization decision support method with budget pacing capability designed for MUs in multi-session forward auction-based federated learning

翻译：基于拍卖的联邦学习（Auction-based Federated Learning, AFL）近年来已成为重要研究领域。现有面向联邦学习模型用户（Model Users, MUs）的策略假定，联邦学习任务所需的全部数据所有者（Data Owners, DOs）必须在训练开始前完成集结。实践中，模型用户可多次触发联邦学习训练过程，因此数据所有者能够在多个模型训练会话中逐步招募。现有面向AFL模型用户的竞价策略无法处理此类场景，多会话AFL问题仍待解决。针对该问题，我们提出面向正向拍卖联邦学习的多会话预算优化策略（MultiBOS-AFL）。基于分层强化学习，MultiBOS-AFL联合优化AFL模型用户的会话间预算调整与会话内竞价，以最大化总效用为目标。在六个基准数据集上的大量实验表明，该方法显著优于七种前沿方案。与最佳基线相比，MultiBOS-AFL平均实现效用提升12.28%，在给定预算下通过拍卖获取的数据量增加14.52%，生成的联邦模型测试准确率提高1.23%。据我们所知，这是首个为多会话正向拍卖联邦学习中的模型用户设计的、具备预算调整能力的预算优化决策支持方法。