International Conference on Computer and Knowledge Engineering

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A Federated Learning-Based Hybrid Deep Learning Framework for Enhanced Human Activity Recognition

Authors :

Jamileh Azmoudeh¹ Sajjad Arghaee² Parisa Valizadeh³ Samaneh Dandani⁴ Iman Havangi⁵ Mohammad Hossein Yaghmaee⁶

1- Ferdowsi university of mashhad 2- Ferdowsi university of mashhad 3- Ferdowsi university of mashhad 4- Ferdowsi university of mashhad 5- Ferdowsi university of mashhad 6- Ferdowsi university of mashhad

Keywords :

Human activity recognition،Federated learning،CNN،LSTM

Abstract :

Human Activity Recognition (HAR) has become increasingly important with the advent of mobile computing and sensor technologies. Traditional HAR systems, relying on centralized data processing and supervised learning techniques, face significant challenges related to data privacy, scalability, and the need for extensive labeled datasets. Federated Learning (FL) has emerged as a promising solution to address these limitations by allowing collective model training without centralizing sensitive user data, thereby enhancing privacy and personalization. This paper introduces a novel framework that integrates FL with hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models to improve the accuracy and robustness of HAR. Our approach leverages the strengths of CNNs in capturing spatial features and LSTMs in modeling temporal dependencies, while maintaining data privacy through FL. Extensive experiments on real-world HAR datasets demonstrate that our proposed framework not only preserves privacy but also achieves high recognition rates with limited labeled data, showcasing its potential for practical applications in healthcare, fitness, and smart home environments.