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Open Access

REVIEW

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

Meenakshi Aggarwal1, Vikas Khullar2,*, Nitin Goyal3
1 Bhagwan Parshuram Institute of Technology, GGSIPU, Rohini, New Delhi, 110089, India
2 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India
3 Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh, Haryana, 123031, India
* Corresponding Author: Vikas Khullar. Email: email
(This article belongs to the Special Issue: Integrating Split Learning with Tiny Models for Advanced Edge Computing Applications in the Internet of Vehicles)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072673

Received 01 September 2025; Accepted 31 October 2025; Published online 01 December 2025

Abstract

The Internet of Vehicles, or IoV, is expected to lessen pollution, ease traffic, and increase road safety. IoV entities’ 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. The approach starts with the IoV learning framework, which includes edge computing, FL, SL, and TinyML, and then proceeds to discuss how these technologies might be integrated. We elucidate the comprehensive operational principles of Federated and split learning by examining and addressing many challenges. 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.

Keywords

Machine learning; federated learning; split learning; TinyML; internet of vehicles

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