  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impact of outdated local updates and enhancing the stability of FL systems.
