Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i.e., servers') FL training tasks. To enhance the efficiency in AFL decision support for stakeholders (i.e., data consumers, data owners, and the auctioneer), intelligent agent-based techniques have emerged. However, due to the highly interdisciplinary nature of this field and the lack of a comprehensive survey providing an accessible perspective, it is a challenge for researchers to enter and contribute to this field. This paper bridges this important gap by providing a first-of-its-kind survey on the Intelligent Agents for AFL (IA-AFL) literature. We propose a unique multi-tiered taxonomy that organises existing IA-AFL works according to 1) the stakeholders served, 2) the auction mechanism adopted, and 3) the goals of the agents, to provide readers with a multi-perspective view into this field. In addition, we analyse the limitations of existing approaches, summarise the commonly adopted performance evaluation metrics, and discuss promising future directions leading towards effective and efficient stakeholder-oriented decision support in IA-AFL ecosystems.

翻译：拍卖式联邦学习（Auction-based Federated Learning, AFL）是联邦学习激励机制设计中一类重要的新兴范式，因其能够公平高效地激励高质量数据所有者参与数据消费者（即服务器）的联邦学习训练任务。为提升AFL对利益相关方（包括数据消费者、数据所有者及拍卖商）决策支持的有效性，基于智能体的技术应运而生。然而，该领域高度跨学科的特性以及缺乏提供可理解视角的综合性综述，使得研究者进入并贡献于该领域面临挑战。本文通过首次系统综述智能体赋能AFL（IA-AFL）文献，填补了这一重要空白。我们提出一种独特的多层次分类法，根据1）服务的利益相关方、2）采用的拍卖机制以及3）智能体的目标三个维度对现有IA-AFL工作进行分类，为读者提供该领域的多视角认知。此外，我们分析了现有方法的局限性，总结了常用性能评估指标，并讨论了面向IA-AFL生态系统中高效且有效的利益相关方决策支持的未来研究方向。