5 papers
Distributed Quasi-Newton Method for Fair and Fast Federated Learning
Shayan Mohajer Hamidi, Linfeng Ye
Federated learning (FL) is a promising technology that enables edge devices/clients to collaboratively and iteratively train a machine learning model under the coordination of a ce…
Coded Deep Learning: Framework and Algorithm
En-hui Yang, Shayan Mohajer Hamidi
The success of deep learning (DL) is often achieved with large models and high complexity during both training and post-training inferences, hindering training in resource-limited…
Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems
Shayan Mohajer Hamidi, En-Hui Yang
Inverse problems are prevalent across various disciplines in science and engineering. In the field of computer vision, tasks such as inpainting, deblurring, and super-resolution ar…
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling
Shayan Mohajer Hamidi, En-Hui Yang
Inverse problems exist in many disciplines of science and engineering. In computer vision, for example, tasks such as inpainting, deblurring, and super resolution can be effectivel…
Rate-Constrained Quantization for Communication-Efficient Federated Learning
Shayan Mohajer Hamidi, Ali Bereyhi
Quantization is a common approach to mitigate the communication cost of federated learning (FL). In practice, the quantized local parameters are further encoded via an entropy codi…