Federated learning (FL) has gained prominence due to heightened concerns over data privacy. Privacy restrictions limit the visibility for data consumers (DCs) to accurately assess the capabilities and efforts of data owners (DOs). Thus, for open collaborative FL markets to thrive, effective incentive mechanisms are key as they can motivate data owners (DOs) to contribute to FL tasks. Contract theory is a useful technique for developing FL incentive mechanisms. Existing approaches generally assume that once the contract between a DC and a DO is signed, it remains unchanged until the FL task is finished. However, unforeseen circumstances might force a DO to be unable to fulfill the current contract, resulting in inefficient utilization of DCs' budgets. To address this limitation, we propose the Renegotiable Contract-Theoretic Incentive Mechanism (RC-TIM) for FL. Unlike previous approaches, it adapts to changes in DOs' behavior and budget constraints by supporting the renegotiation of contracts, providing flexible and dynamic incentives. Under RC-TIM, an FL system is more adaptive to unpredictable changes in the operating environment that can affect the quality of the service provided by DOs. Extensive experiments on three benchmark datasets demonstrate that RC-TIM significantly outperforms four state-of-the-art related methods, delivering up to a 45.76% increase in utility on average.

翻译：联邦学习（FL）因日益增长的数据隐私关切而备受关注。隐私限制使得数据消费者（DCs）难以准确评估数据所有者（DOs）的能力与努力程度。因此，对于开放的协作式联邦学习市场而言，有效的激励机制至关重要，因其能激励数据所有者（DOs）为联邦学习任务做出贡献。契约理论是设计联邦学习激励机制的有效工具。现有方法通常假设数据消费者与数据所有者之间的契约一旦签订，在联邦学习任务完成前将保持不变。然而，不可预见的情况可能导致数据所有者无法履行当前契约，从而造成数据消费者预算的低效利用。为克服这一局限，本文提出一种适用于联邦学习的可重协商契约理论激励机制（RC-TIM）。与先前方法不同，该机制通过支持契约重协商来适应数据所有者行为与预算约束的变化，提供灵活且动态的激励。在RC-TIM框架下，联邦学习系统能更好地适应运行环境中可能影响数据所有者服务质量的不可预测变化。在三个基准数据集上的大量实验表明，RC-TIM显著优于四种先进的现有方法，平均效用提升最高可达45.76%。