Federated Learning (FL) is a paradigm that enables collaborative training while keeping data localized, avoiding direct sharing with a central server. However, Non-IID (Non-Independent and Identically Distributed) data across clients presents a significant challenge for FL, compromising the global model performance. In this paper, we propose Entropy-based Loss Function Selection (ELFS), designed to enhance global model accuracy by selectively adapting the loss function based on the entropy of each client’s data distribution. ELFS leverages two core steps before local training to determine the appropriate loss function: 1) entropy calculation and 2) adaptive loss function selection. The entropy calculation step quantifies the data distribution of each client based on label frequencies. Subsequently, in the adaptive loss function selection step, each client selects an appropriate loss function to mitigate the impact of data imbalance. Our experimental results on Non-IID datasets, CIFAR-10 and CIFAR-100, demonstrate that ELFS improves global model accuracy by up to 16.13% compared to conventional FL methods, such as FedAvg, FedProx, and FedPer. By optimizing entropy thresholds, we further demonstrate the importance of fine-tuning hyperparameters to maximize accuracy. Moreover, ELFS offers flexibility for integrating additional loss functions, providing potential for further performance improvements in handling Non-IID data.

This paper focuses on the problem of forecasting daily traffic of new roads, where very little data is available for prediction. We propose a novel prediction model based on Generative Adversarial Networks (GAN) that learns the subtle patterns of the changes in the traffic flow according to the various contextual factors. Then the trained generator makes a prediction via generating a realistic traffic flow data of a target new road given its weather and day type. Both the quantitative and qualitative results of our extensive experiments indicate the effectiveness of our method.

Many global automakers strive to develop technologies towards the next-generation of intelligent transportation systems (ITS). One of the primary goals of ITS is predicting future traffic speeds to optimize a driver's route, which can lead to not only alleviating traffic flow but also increasing user satisfaction with an ITS service. While prior studies have applied deep learning models to traffic speed prediction and improved model performance, existing models did not well capture abrupt speed changes. In this paper, we propose a novel model, named as adversarial prediction of traffic speed (APOTS), based on adversarial training, data augmentation, and hybrid deep learning modeling. Through the experiments with real traffic data provided by Hyundai Motor Company, we demonstrate that APOTS effectively learns dynamics of traffic speed changes and predicts traffic speed up to 40% higher in accuracy than existing prediction models.