Enriching Security in Federated Learning and IoT - A Systematic Review
Abstract
In this era of ubiquitous connectivity and smart systems, combining Federated Learning (FL) and the Internet of Things (IoT) symbolizes a revolutionary trend in edge-based intelligence. FL offers an exclusive decentralized feature for machine learning wherein the data resides on IoT devices improving privacy and also minimizes the huge data exchange. Integration of FL with IoT covers wide range of applications with the involvement of real time analytics. Still there exists obstacles from this integration like attacks on IoT devices. To counteract these attacks or threats several security mechanisms have evolved with different approaches. These approaches focus on enhancing the FL-IoT environment by preserving data confidentiality or security by checking the authentication of users using IoT devices. With the advancement in the development of edge devices, maintaining the synchronization between FL and IoT is critical for achieving desirable intelligence in next generation networks.
How strong is the evidence?
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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