In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural
Networks (DNNs) that addresses the challenge of optimizing the output distortion …

One main challenge in federated learning is the large communication cost of exchanging
weight updates from clients to the server at each round. While prior work has made great …

Federated learning allows for cooperative training among distributed clients by sharing their
locally learned model parameters, such as weights or gradients. However, as model size …

We study the mean estimation problem under communication and local differential privacy
constraints. While previous work has proposed order-optimal algorithms for the same …

We consider the attributes of a point cloud as samples of a vector-valued volumetric function
at discrete positions. To compress the attributes given the positions, we compress the …

Large Language Models (LLMs) have recently gained popularity due to their impressive few-
shot performance across various downstream tasks. However, fine-tuning all parameters …

Vision transformers (ViTs) have emerged as a promising alternative to convolutional neural
networks (CNNs) for various image analysis tasks, offering comparable or superior …

Video represents the majority of internet traffic today, driving a continual race between the
generation of higher quality content, transmission of larger file sizes, and the development of …

Motivated by high resource costs of centralized machine learning schemes as well as data
privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on …

State-of-the-art performance for many edge applications is achieved by deep neural
networks (DNNs). Often, these DNNs are location-and time-sensitive, and must be delivered …