FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

In recent years, the data available from IoT devices have increased rapidly. Using a machine learning solution to detect faults in these devices requires the release of device data to a central server. However, these data typically contain sensitive information, leading to the need for privacy-preserving distributed machine learning solutions, such as federated learning, where a model is trained locally on the edge device, and only the trained model weights are shared with a central server. Device failure data are typically imbalanced, i.e., the number of failures is minimal compared to the number of normal samples. Therefore, re-balancing techniques are needed to improve the performance of a machine learning model. In this paper, we present FLY-SMOTE, a new approach to re-balance the data in different non-IID scenarios by generating synthetic data for the minority class in supervised learning tasks using a modified SMOTE method. Our approach takes k samples from the minority class and generates Y new synthetic samples based on one of the nearest neighbors of each k sample. An experimental campaign on a real IoT dataset and three well-known public datasets show that the proposed solution improves the balance accuracy without compromising the model's accuracy.

Automated summary

In recent years, the amount of data available from IoT devices has increased rapidly. To protect privacy, distributed machine learning solutions are needed. However, device failure data are typically imbalanced, requiring re-balancing techniques. This paper proposes a new approach called FLY-SMOTE, which rebalances data by generating synthetic data, and experimental results demonstrate its effectiveness.