FLight: A Lightweight Federated Learning Framework in Edge and Fog Computing

TL;DR

FLight is a lightweight federated learning framework designed for diverse Edge and Fog computing devices, enabling efficient, privacy-preserving distributed model training with minimal overhead.

Contribution

The paper introduces FLight, a novel lightweight FL framework tailored for resource-constrained Edge/Fog devices, integrating both synchronous and asynchronous models and a heuristic worker selection algorithm.

Findings

FLight achieves high training efficiency on diverse devices.

The framework effectively manages FL tasks with minimal system overhead.

Experimental results demonstrate the framework's suitability for IoT environments.

Abstract

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.