Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently impair the learning performance of the global model owned by the model owner (i.e., the cloud server). However, strategies to motivate clients with high-quality contributions to join the FL training process and share trained model updates during CLPs remain underexplored. Additionally, existing incentive mechanisms in FL treat all training periods equally, which consequently fails to motivate clients to participate early. Compounding this challenge is the cloud's limited knowledge of client training capabilities due to privacy regulations, leading to information asymmetry. Therefore, in this article, we propose a time-aware incentive mechanism, called Right Reward Right Time (R3T), to encourage client involvement, especially during CLPs, to maximize the utility of the cloud in FL. Specifically, the cloud utility function captures the trade-off between the achieved model performance and payments allocated for clients' contributions, while accounting for clients' time and system capabilities, efforts, joining time, and rewards. Then, we analytically derive the optimal contract for the cloud and devise a CLP-aware mechanism to incentivize early participation and efforts while maximizing cloud utility, even under information asymmetry. By providing the right reward at the right time, our approach can attract the highest-quality contributions during CLPs. Simulation and proof-of-concept studies show that R3T increases cloud utility and is more economically effective than benchmarks. Notably, our proof-of-concept results show up to a 47.6% reduction in the total number of clients and up to a 300% improvement in convergence time while reaching competitive test accuracies compared with incentive mechanism benchmarks.

本研究解决了联邦学习中关键学习阶段（CLPs）内低质量贡献对全局模型学习效果的负面影响的问题。我们提出了一种基于时间的激励机制“正确奖励正确时机”（R3T），旨在鼓励客户在关键学习阶段积极参与并分享高质量的模型更新。实验结果表明，该机制显著提高了云的效用，并在多项指标上超越了目前的激励机制基准。

正确奖励在联邦学习中的最佳时机