Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Federated Learning (FL) models often experience client drift caused by heterogeneous data,
where the distribution of data differs across clients. To address this issue, advanced …

The performance of intelligent vehicles (IVs) in object detection relies not only on the design
or scale of the CNN model they use but also on how effectively they share their acquired …

Federated learning (FL) is now considered a critical method for breaking down data silos.
However, data encryption can significantly increase computing time, limiting its large-scale …

Advances in hardware, proliferation of compute at the edge, and data creation at
unprecedented scales have made federated learning (FL) necessary for the next leap …