Lawrence Berkeley National Laboratory

While federated learning (FL) has gained great attention for mobile and Internet of Things (IoT) computing with the benefits of scalable cooperative learning and privacy protection capabilities, there still exist a great deal of technical challenges to make it practically deployable. For instance, the distribution of the training process to a myriad of devices limits the classification performance of machine learning (ML) algorithms, often showing a significantly degraded accuracy compared to centralized learning. In this paper, we investigate the problem of performance limitation under FL and present the benefit of data augmentation with an application of anomaly detection using an IoT dataset. Our initial study reveals that one of the critical reasons for the performance degradation is that each device sees only a small fraction of data (that it generates), which limits the efficacy of the local ML model (constructed by the device). This becomes more critical if the data holds the class imbalance problem, observed not infrequently in practice (e.g., a small fraction of anomalies). Moreover, device heterogeneity with respect to data quantity is an open challenge in FL. Based on these observations, we examine the impact of data augmentation on detection performance in FL settings (both homogeneous and heterogeneous). Our experimental results show that even a simple random oversampling can improve detection performance with manageable learning complexity.