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.

提出一种基于多智能体强化学习的新颖机制来优化跨领域联邦学习的激励机制，从历史潜在博弈中学习最优的数据贡献策略，以有效改进组织的长期收益。