Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm facilitating collaborative training across multiple clients without sharing local data. Despite advancements in edge device capabilities, communication bottlenecks present challenges in aggregating a large number of clients; only a portion of the clients can update their parameters upon each global aggregation. This phenomenon introduces the critical challenge of stragglers in FL and the profound impact of client scheduling policies on global model convergence and stability. Existing scheduling strategies address staleness but predominantly focus on either timeliness or content. Motivated by this, we introduce the novel concept of Version Age of Information (VAoI) to FL. Unlike traditional Age of Information metrics, VAoI considers both timeliness and content staleness. Each client's version age is updated discretely, indicating the freshness of information. VAoI is incorporated into the client scheduling policy to minimize the average VAoI, mitigating the impact of outdated local updates and enhancing the stability of FL systems.

翻译：联邦学习（FL）作为一种隐私保护的机器学习范式，支持多个客户端在不共享本地数据的情况下进行协同训练。尽管边缘设备能力不断提升，通信瓶颈仍对聚合大量客户端构成挑战：每次全局聚合时仅有部分客户端能够更新参数。这一现象带来了FL中掉队者的关键问题，以及客户端调度策略对全局模型收敛性与稳定性的深远影响。现有调度策略虽能处理陈旧性问题，但主要侧重于时效性或内容性。受此启发，我们将信息版本老化（VAoI）这一新概念引入FL。与传统信息年龄指标不同，VAoI同时考量时效性和内容陈旧性。各客户端的版本年龄通过离散方式更新，表征信息的鲜活性。VAoI被纳入客户端调度策略，旨在最小化平均VAoI，从而缓解过时本地更新的影响，增强FL系统的稳定性。