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

基于层次强化学习的多会话向前拍卖联邦学习（MultiBOS-AFL）通过优化会话间预算节奏和会话内竞标，旨在最大化总效用，实验证明其明显优于七种最先进方法，平均而言，MultiBOS-AFL 比最佳基准线的效用高出 12.28％、在给定预算内通过拍卖获得的数据多出 14.52％，以及最终 FL 模型的测试准确性比最佳基准线高出 1.23％。据我们所知，这是第一个设计用于多会话向前拍卖联邦学习中的带有预算节奏能力的预算优化决策支持方法。