GAN-Enhanced Vertical Federated Learning System for WHAR with non-IID Data

Vertical Federated Learning (VFL) presents a promising approach for training machine learning models across wearable Internet of Things (IoT) devices while keeping the data local. However, non-Independent and Identically Distributed (IID) data from heterogeneous IoT devices lead to degraded performance of Wearable Human Activity Recognition (WHAR) models due to overfitting. To address non-IID data, this paper introduces the Federated-Multimodal GAN (FM-GAN) approach, leveraging Generative Adversarial Networks (GANs) models to generate an IID distribution of data for balancing multi-modal data.