The number of Internet of Things (IoT) applications, especially latency-sensitive ones, have been significantly increased. So, Cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy the requirements of IoT applications. Edge/Fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the network, offering low latency, reduced network traffic, and higher bandwidth. The Edge/Fog resources are often less powerful compared to Cloud, and IoT data is dispersed among many geo-distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well-suited to Edge/Fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on Edge/Fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system. Accordingly, in this paper, we propose a lightweight FL framework, called FLight, to be deployed on a diverse range of devices, ranging from resource limited Edge/Fog devices to powerful Cloud servers. FLight is implemented based on the FogBus2 framework, which is a containerized distributed resource management framework. Moreover, FLight integrates both synchronous and asynchronous models of FL. Besides, we propose a lightweight heuristic-based worker selection algorithm to select a suitable set of available workers to participate in the training step to obtain higher training time efficiency. The obtained results demonstrate the efficiency of the FLight.

翻译：物联网应用的数量，尤其是延迟敏感型应用，已显著增长。因此，作为物联网主要支撑技术之一且提供集中式服务的云计算，无法单独满足物联网应用的需求。边缘/雾计算作为一种分布式计算范式，在网络边缘处理和存储物联网数据，具有低延迟、降低网络流量和更高带宽的优势。与云相比，边缘/雾资源的计算能力通常较弱，且物联网数据分散于众多地理分布广泛的服务器中。因此，联邦学习（一种使多个分布式服务器能够在不交换原始数据的情况下协同构建模型的机器学习方法）非常适用于数据隐私至关重要的边缘/雾计算环境。此外，为管理边缘/雾计算环境中的不同联邦学习任务，需要一个轻量级资源管理框架，以在不给系统带来显著开销的前提下管理各类传入的联邦学习任务。据此，本文提出一种名为FLight的轻量级联邦学习框架，可部署于从资源受限的边缘/雾设备到高性能云服务器的多种设备上。FLight基于FogBus2框架实现，后者是一个容器化的分布式资源管理框架。此外，FLight集成了同步和异步两种联邦学习模型。同时，我们提出一种轻量级启发式工作节点选择算法，用于从可用工作节点中选取合适子集参与训练步骤，以提高训练时间效率。实验结果证明了FLight的有效性。