Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where data are created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and evaluating FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to evaluate FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).

中文翻译：

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使用类似边缘的环境测试联合学习算法的框架


联邦学习 （FL） 是一种机器学习范式，其中许多客户合作训练单个集中式模型，同时保持其数据的私密性和分散性。联邦学习通常用于边缘计算，它涉及将计算机工作负载（硬件和软件）放置在尽可能靠近边缘的位置，即创建数据和执行操作的地方，从而实现更快的响应时间、更高的数据隐私并降低数据传输成本。然而，由于客户端的数据分布/内容的异构性，准确评估本地模型在全局集中式模型聚合中的贡献并非易事。这是 FL 中一个主要挑战的示例，通常称为数据不平衡或类不平衡。一般来说，由于系统的分布式特性，测试和评估 FL 算法可能是一项非常困难和复杂的任务。在这项工作中，提出并实现了一个框架，以更简单和可扩展的方式评估 FL 算法。该框架在由容器编排平台（即 Kubernetes）管理的类似边缘的分布式环境中进行评估。