  Auction-based Federated Learning (AFL) has attracted extensive research
interest due to its ability to motivate data owners (DOs) to join FL through
economic means. While many existing AFL methods focus on providing decision
support to model users (MUs) and the AFL auctioneer, decision support for data
owners remains open. To bridge this gap, we propose a first-of-its-kind
agent-oriented joint Pricing, Acceptance and Sub-delegation decision support
approach for data owners in AFL (PAS-AFL). By considering a DO's current
reputation, pending FL tasks, willingness to train FL models, and its trust
relationships with other DOs, it provides a systematic approach for a DO to
make joint decisions on AFL bid acceptance, task sub-delegation and pricing
based on Lyapunov optimization to maximize its utility. It is the first to
enable each DO to take on multiple FL tasks simultaneously to earn higher
income for DOs and enhance the throughput of FL tasks in the AFL ecosystem.
Extensive experiments based on six benchmarking datasets demonstrate
significant advantages of PAS-AFL compared to six alternative strategies,
beating the best baseline by 28.77% and 2.64% on average in terms of utility
and test accuracy of the resulting FL models, respectively.
