FSL-TM: Review onthe Integration of Federated Split Learning with TinyML in the Internet of Vehicles

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dc.contributor.authorGoyal, Nitin
dc.date.accessioned2026-03-27T09:48:32Z
dc.date.available2026-03-27T09:48:32Z
dc.date.issued2026
dc.description.abstractThe Internet of Vehicles, or IoV, is expected to lessen pollution, ease traffic, and increase road safety. IoVentities’ interconnectedness, however, raises the possibility of cyberattacks, which can have detrimental effects. IoV systems typically send massive volumes of raw data to central servers, which may raise privacy issues. Additionally, model training on IoV devices with limited resources normally leads to slower training times and reduced service quality. We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning (TinyML) approach, which operates on IoV edge devices without sharing sensitive raw data. Specifically, we focus on integrating split learning (SL) with federated learning (FL) and TinyML models. FL is a decentralised machine learning (ML) technique that enables numerous edge devices to train a standard model while retaining data locally collectively. The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain, coupled with FL and TinyML. Theapproachstarts with the IoV learning framework, which includes edge computing,FL,SL,andTinyML,andthenproceedstodiscusshowthesetechnologiesmightbeintegrated.Weelucidate thecomprehensiveoperationalprinciplesofFederatedandsplitlearningbyexaminingandaddressingmanychallenges. We subsequently examine the integration of SL with FL and various applications of TinyML. Finally, exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM. It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes, thereby obviating the necessity for centralised data aggregation, which presents considerable privacy threats. The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL, hence facilitating advancement in this swiftly progressing domain.
dc.identifier.urihttp://cuh.ndl.gov.in/handle/123456789/1856
dc.language.isoen
dc.titleFSL-TM: Review onthe Integration of Federated Split Learning with TinyML in the Internet of Vehicles
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