迈向可持续的联邦学习生态系统：一种实用的最小核心机制用于收益分配
Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation
AI总结：
本文提出一种基于最小核心机制的实用收益分配框架，用于促进联邦学习生态系统的可持续发展。
摘要：
随着网络范式和应用的不断涌现，越来越多的场景依赖联邦学习（FL）来实现协作智能的同时保护隐私。然而，此类协作环境的可持续性取决于一个公平且稳定的收益分配机制。聚焦于联盟稳定性，本文引入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组中的最大不满来优先维护联邦的凝聚力，确保没有参与者有退出的激励。为了将这一博弈论概念适应于实际的大规模网络，我们提出了一种流线型的实现方法，采用基于栈的修剪算法，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显示，我们的机制能够正确识别关键贡献者和战略联盟。结果证实，实际的LC框架促进了稳定的协作，并促进了可持续的FL生态系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
