Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4X, time efficiency by 8X, and reduces costs by more than 2X compared to extant cloud-based static methodologies.

翻译：联邦学习（FL）是一种通过数据源端计算来解决隐私保护和数据传输成本的机器学习方法。该方法尤其适用于边缘计算与物联网场景，其中FL的聚合服务器部署在资源受限的边缘数据中心，以降低通信成本。现有的云基聚合方案在边缘端存在资源效率低下与成本高昂的问题，导致可扩展性不足与延迟较高。为解决上述挑战，本研究针对物联网与边缘应用不断变化的需求，对既有及新型聚合方法展开比较分析。本工作首次提出自适应边缘端FL聚合器，使用户能够管理成本与效率之间的权衡。大量对比分析表明，与现有云基静态方法相比，该设计将可扩展性提升高达4倍，时间效率提升8倍，并将成本降低超过2倍。