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 the data is being 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 assessing 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 assess 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).

翻译：联邦学习（Federated Learning, FL）是一种机器学习范式，其中多个客户端在保持数据私有化和去中心化的同时，协作训练一个单一的集中式模型。FL通常应用于边缘计算，即将计算负载（包括硬件和软件）尽可能部署在靠近数据生成和操作发生的边缘位置，从而实现更快的响应时间、更强的数据隐私保护以及更低的数据传输成本。然而，由于客户端的数据分布或内容具有异构性，在全局集中式模型聚合中准确评估本地模型的贡献并非易事。这是联邦学习中的一个主要挑战，通常被称为数据不平衡或类别不平衡问题。总体而言，由于系统的分布式特性，测试和评估联邦学习算法可能是一项非常困难且复杂的任务。本研究提出并实现了一个框架，旨在以更简便且可扩展的方式评估联邦学习算法。该框架在一个由容器编排平台（即Kubernetes）管理的分布式边缘计算环境中进行了评估。