  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.
