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.

该论文提供了首次的智能代理人对拍卖联邦学习（IA-AFL）文献的综述，通过提出一个独特的多层次分类法，将 IA-AFL 的现有研究组织成为以利益相关者、采用的拍卖机制和代理人目标为基础的多角度视角，同时分析了现有方法的局限性、总结了常用的性能评估指标，并探讨了未来有前景的方向，为 IA-AFL 生态系统中的有效而高效的利益相关者导向的决策支持提供了基础。