This paper investigates mechanism design for decision-aware collaboration via federated learning (FL) platforms. Our framework consists of a digital platform and multiple decision-aware agents, each endowed with proprietary data sets. The platform offers an infrastructure that enables access to the data, creates incentives for collaborative learning aimed at operational decision-making, and conducts FL to avoid direct raw data sharing. The computation and communication efficiency of the FL process is inherently influenced by the agent participation equilibrium induced by the mechanism. Therefore, assessing the system's efficiency involves two critical factors: the surplus created by coalition formation and the communication costs incurred across the coalition during FL. To evaluate the system efficiency under the intricate interplay between mechanism design, agent participation, operational decision-making, and the performance of FL algorithms, we introduce a multi-action collaborative federated learning (MCFL) framework for decision-aware agents. Under this framework, we further analyze the equilibrium for the renowned Shapley value based mechanisms. Specifically, we examine the issue of false-name manipulation, a form of dishonest behavior where participating agents create duplicate fake identities to split their original data among these identities. By solving the agent participation equilibrium, we demonstrate that while Shapley value effectively maximizes coalition-generated surplus by encouraging full participation, it inadvertently promotes false-name manipulation. This further significantly increases the communication costs when the platform conducts FL. Thus, we highlight a significant pitfall of Shapley value based mechanisms, which implicitly incentivizes data splitting and identity duplication, ultimately impairing the overall efficiency in FL systems.

翻译：本文研究通过联邦学习（FL）平台实现决策感知协作的机制设计问题。我们的框架包含一个数字平台和多个具有决策感知能力的智能体，每个智能体都拥有专属数据集。平台提供数据访问基础设施，为面向运营决策的协作学习创造激励，并通过FL避免原始数据的直接共享。FL过程的计算与通信效率本质上受到机制所诱导的智能体参与均衡的影响。因此，评估系统效率涉及两个关键因素：联盟形成产生的剩余价值以及FL过程中联盟内产生的通信成本。为评估机制设计、智能体参与、运营决策与FL算法性能之间复杂交互作用下的系统效率，我们提出了一种面向决策感知智能体的多行动协作联邦学习（MCFL）框架。在此框架下，我们进一步分析了著名的Shapley值机制的均衡状态。具体而言，我们研究了虚假身份操纵问题——一种不诚实行为，即参与智能体通过创建重复虚假身份，将原始数据分散到这些身份中。通过求解智能体参与均衡，我们证明：尽管Shapley值能通过鼓励完全参与有效最大化联盟产生的剩余，但它无意中助长了虚假身份操纵。这进一步显著增加了平台运行FL时的通信成本。因此，我们揭示了Shapley值机制的一个重大隐患：其隐式激励数据分割与身份重复，最终损害了FL系统的整体效率。