Small models, big impact: A review on the power of lightweight Federated Learning

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-23 DOI:10.1016/j.future.2024.107484

引用次数: 0

Abstract

Federated Learning (FL) enhances Artificial Intelligence (AI) applications by enabling individual devices to collaboratively learn shared models without uploading local data with third parties, thereby preserving privacy. However, implementing FL in real-world scenarios presents numerous challenges, especially with IoT devices with limited memory, diverse communication conditions, and varying computational capabilities. The research community is turning to lightweight FL, the new solutions that optimize FL training, inference, and deployment to work efficiently on IoT devices. This paper reviews lightweight FL, systematically organizing and summarizing the related techniques based on its workflow. Finally, we indicate potential problems in this area and suggest future directions to provide valuable insights into the field.

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小模型，大影响：评述轻量级联合学习的威力

联盟学习（Federated Learning，FL）可使单个设备在不向第三方上传本地数据的情况下协作学习共享模型，从而保护隐私，从而增强人工智能（AI）应用。然而，在现实世界场景中实施群集学习面临诸多挑战，尤其是物联网设备内存有限、通信条件各异、计算能力参差不齐。研究界正在转向轻量级 FL，即优化 FL 训练、推理和部署，以便在物联网设备上高效工作的新解决方案。本文回顾了轻量级 FL，根据其工作流程系统地整理和总结了相关技术。最后，我们指出了这一领域的潜在问题，并提出了未来的发展方向，以便为这一领域提供有价值的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.