Emerging network paradigms and applications increasingly rely on federated learning (FL) to enable collaborative intelligence while preserving privacy. However, the sustainability of such collaborative environments hinges on a fair and stable payoff allocation mechanism. Focusing on coalition stability, this paper introduces a payoff allocation framework based on the least core (LC) concept. Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction among all potential subgroups, ensuring that no participant has an incentive to break away. To adapt this game-theoretic concept to practical, large-scale networks, we propose a streamlined implementation with a stack-based pruning algorithm, effectively balancing computational efficiency with allocation precision. Case studies in federated intrusion detection demonstrate that our mechanism correctly identifies pivotal contributors and strategic alliances. The results confirm that the practical LC framework promotes stable collaboration and fosters a sustainable FL ecosystem.

翻译：新兴网络范式与应用日益依赖联邦学习（FL）来实现协作智能并保护隐私。然而，此类协作环境的可持续性取决于公平且稳定的收益分配机制。本文聚焦于联盟稳定性，提出了一种基于最小核心（LC）概念的收益分配框架。与传统方法不同，LC通过最小化所有潜在子群的最大不满度来优先保障联邦的凝聚力，确保任何参与者均无脱离动机。为将这一博弈论概念适配于实际的大规模网络，我们提出了一种采用基于堆栈的剪枝算法的精简实现方案，有效平衡了计算效率与分配精度。在联邦入侵检测中的案例研究表明，本机制能够准确识别关键贡献者与战略联盟。结果证实，该实用LC框架能够促进稳定协作并培育可持续的联邦学习生态系统。