发表机构
Nokia Bell Labs（诺基亚贝尔实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对托管Wi-Fi网络中聚类联邦学习的信息性集群识别难题，提出两步聚类的新型CFL工具，在Wi-Fi流量预测中实现了最优预测性能与聚类策略中最低通信和能源开销。
AI 中文摘要
集中式托管Wi-Fi解决方案正越来越多地利用分布式人工智能（AI）来预测接入点（AP）的关键运营统计数据，并主动优化网络性能。在这种背景下，聚类联邦学习（CFL）是一种合适的方法，可生成多个AI模型，以考虑AP数据分布的不同统计特性。然而，识别用于分组AP模型的信息性集群仍然是一个重大挑战。在本文中，我们通过提出一种集成了两步聚类过程的新型CFL工具来解决此问题。首先，基于一组最小的期望聚类标准生成并过滤多个聚类解决方案。随后，如果没有解决方案满足足够的质量指标，则通过聚合所有AP模型生成全局模型；否则，选择使最小集群的信息性（通过微分熵量化）最大化的聚类解决方案作为最终结果。我们针对Wi-Fi流量预测问题的结果表明，所开发的CFL工具在所有评估的分布式策略中实现了最佳预测性能，并且在聚类策略中具有最低的通信和能源 footprint，仅在其显著提高准确性的情况下，成本才超过单模型联邦学习（FL）的成本。
英文摘要
Centrally-managed Wi-Fi solutions are increasingly leveraging Distributed Artificial Intelligence (AI) to predict key operational statistics of Access Points (APs) and proactively optimize network performance. In this context, Clustered Federated Learning (CFL) represents a fitting methodology, enabling the generation of multiple AI models that account for diverse statistical properties of the APs data distribution. However, identifying informative clusters for grouping APs models remains a significant challenge. In this paper, we address this problem by proposing a novel CFL tool integrating a two step clustering procedure. Initially, multiple clustering solutions are generated and filtered based on a minimum set of desired clustering criteria. Subsequently, if no solutions meet sufficient quality metrics, a global model is produced by aggregating all AP models. Otherwise, the final clustering solution is selected as the one that maximizes the informativeness (quantified via differential entropy) for the smallest cluster. Our results, focusing on a Wi-Fi traffic prediction problem, demonstrate that the developed CFL tool achieves the best predictive performance among all evaluated distributed strategies and the lowest communication and energy footprint among the clustered ones, exceeding the cost of single-model FL only in the regimes where it markedly improves accuracy.
Commentssubmitted to IEEE for possible publication
