Federated Learning Frameworks: A survey

Abstract

The presence of numerous federated Learning (FL) frameworks reflects the intricate nature of this burgeoning field. These frameworks are shaped by a wide spectrum of applications, datasets, and performance benchmarks in real-world scenarios. Each framework is meticulously crafted to tackle distinct challenges and meet specific requirements, offering a repertoire of features and functionalities tailored to accommodate diverse use cases. As the demand for FL surges across industries, the proliferation of multiple frameworks affords researchers and practitioners the flexibility to select appropriate platforms. This paper presents a comprehensive survey of the most recent and common FL frameworks. Through this survey, we provide a comprehensive analysis of an exhaustive list of FL frameworks according to three main axes: model, performance and security. After defining the objectives of each axis, we present a comparative study of these frameworks in order to offer guidelines for selecting the appropriate framework based on specific application needs.