Federated learning in edge AI: a systematic review of applications, privacy challenges, and preservation techniques
Abstract
Edge artificial intelligence (Edge AI) involves the implementation of AI algorithms and models directly on local edge devices, such as sensors or internet of things (IoT) devices. This allows for immediate processing and analysis of data without the need for continuous dependence on cloud infrastructure. Concerns about privacy have grown importance in recent years for businesses looking to uphold end-user expectations and safeguard business models. Federated learning (FL) has emerged as a novel approach to enhance privacy. To improve generalization qualities, FL trains local models on local data. These models then collaborate to update a global model. Each edge device (like smartphones, IoT sensors, or autonomous vehicles) trains a local model on its own data. This local training helps in capturing data patterns specific to each device or node. Poisoning, backdoors, and generative adversarial network (GAN)-based attacks are currently the main security risk. Nevertheless, the biggest threat to FL’s privacy is from inference-based assaults such as model inversion attacks, differential privacy shortcomings and FL utilizes blockchain and cryptography technologies to improve privacy on edge devices. This paper presents a thorough examination of the current literature on this subject. In more detail, we study the background of FL and its different existing applications, types, privacy threats and its techniques for privacy preservation.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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