Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine
Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments …

The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …

Abstract Cross-device Federated Learning (FL) faces significant challenges where low-end
clients that could potentially make unique contributions are excluded from training large …

Conditional stochastic optimization has found applications in a wide range of machine
learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the …

To develop the most appropriate control strategy and monitor, maintain, and evaluate the
traffic performance of the freeway weaving areas, state and local Departments of …

The study of generative models is a promising branch of deep learning techniques, which
has been successfully applied to different scenarios, such as Artificial Intelligence and the …

Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down the …

The increasing complexity of deep neural networks poses significant barriers to
democratizing them to resource-limited edge devices. To address this challenge, split …

Current techniques for deep neural network (DNN) pruning often involve intricate multi-step
processes that require domain-specific expertise making their widespread adoption …