Cross-silo federated learning (FL) is a typical FL that enables organizations(e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is key to encouraging organizations to contribute data. However, existing works on incentivizing cross-silo FL lack consideration of the environmental dynamics (e.g., precision of the trained global model and data owned by uncertain clients during the training processes). Moreover, most of them assume that organizations share private information, which is unrealistic. To overcome these limitations, we propose a novel adaptive mechanism for cross-silo FL, towards incentivizing organizations to contribute data to maximize their long-term payoffs in a real dynamic training environment. The mechanism is based on multi-agent reinforcement learning, which learns near-optimal data contribution strategy from the history of potential games without organizations' private information. Experiments demonstrate that our mechanism achieves adaptive incentive and effectively improves the long-term payoffs for organizations.

翻译：跨孤岛联邦学习是一种典型的联邦学习范式，它使组织（如金融机构或医疗机构）能够在隔离数据上训练全局模型。合理的激励是鼓励组织贡献数据的关键。然而，现有关于跨孤岛联邦学习激励的研究缺乏对环境动态性（例如，训练过程中训练全局模型的精度以及不确定客户端所拥有的数据）的考虑。此外，大多数研究假设组织共享私有信息，这在实际中并不现实。为克服这些局限，我们提出了一种新颖的跨孤岛联邦学习自适应机制，旨在激励组织贡献数据，从而在真实的动态训练环境中最大化其长期收益。该机制基于多智能体强化学习，能够在不获取组织私有信息的前提下，从势博弈的历史中学习近优的数据贡献策略。实验表明，我们的机制实现了自适应激励，并有效提升了组织的长期收益。