Federated Split Learning for Human Activity Recognition with Differential Privacy

TL;DR

This paper introduces a Federated Split Learning framework with Differential Privacy for human activity recognition, improving accuracy, privacy, and communication efficiency over traditional federated learning methods on real datasets.

Contribution

It presents a novel FSL-DP framework that enhances HAR accuracy, privacy protection, and training efficiency compared to existing federated learning approaches.

Findings

FSL-DP outperforms traditional FL in accuracy and loss metrics.

FSL-DP achieves faster communication times per training round.

The framework effectively balances privacy and model performance.

Abstract

This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyroscope data, achieving significant improvements in HAR accuracy. The evaluation includes a detailed comparison between traditional Federated Learning (FL) and our FSL framework, showing that the FSL framework outperforms FL models in both accuracy and loss metrics. Additionally, we examine the privacy-performance trade-off under different data settings in the DP mechanism, highlighting the balance between privacy guarantees and model accuracy. The results also indicate that our FSL framework achieves faster communication times per training round compared to traditional FL, further emphasizing its efficiency and effectiveness. This work provides valuable insight and a novel framework which was tested on a real-life dataset.