Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
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DOI：
10.1109/bigdata59044.2023.10386691
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
A. Khan;Yuze Li;Xinran Wang;Sabaat Haroon;Haider Ali;Yue Cheng;A. Butt;Ali Anwar
中科院分区：
文献类型：
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作者：
A. Khan;Yuze Li;Xinran Wang;Sabaat Haroon;Haider Ali;Yue Cheng;A. Butt;Ali Anwar
联邦学习（FL）是一种机器学习方法，通过在源头计算数据来解决隐私和数据传输成本问题。它特别受边缘和物联网应用的欢迎，在这些应用中，FL的聚合服务器位于资源受限的边缘数据中心，以降低通信成本。现有的基于云的聚合器解决方案在Edge上资源效率低下且价格昂贵，导致低可扩展性和高延迟。为了应对这些挑战，本研究在物联网和边缘应用不断变化的需求下比较了以前和新的聚合方法。这项工作是第一个在Edge上提出自适应FL聚合器，使用户能够管理成本和效率之间的权衡。广泛的比较分析表明，与现有的基于云的静态方法相比，该设计将可扩展性提高了4倍，时间效率提高了8倍，成本降低了2倍以上。
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 4$\times$, time efficiency by 8$\times$, and reduces costs by more than 2$\times$ compared to extant cloud-based static methodologies.