Search Results for author:
Found 17 papers, 1 papers with code
Bayesian Federated Model Compression for Communication and Computation Efficiency
We propose a decentralized Turbo variational Bayesian inference (D-Turbo-VBI) FL framework where we firstly propose a hierarchical sparse prior to promote a clustered sparse structure in the weight matrix.
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system.
Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach
In this paper, we propose a novel planning and operation model minimizing the system-level carbon emissions via sitting and operating geographically shiftable resources.
Mode Connectivity and Data Heterogeneity of Federated Learning
Empirically, reducing data heterogeneity makes the connectivity on different paths more similar, forming more low-error overlaps between client and global modes.
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality
In this paper, we tackle this desynchronization using a continual RL framework that facilitates the resource allocation dynamically for MEC-enabled VR content streaming.
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Based on these findings from our loss landscape visualization and loss decomposition, we propose utilizing iterative moving averaging (IMA) on the global model at the late training phase to reduce its deviation from the expected minimum, while constraining client exploration to limit the maximum distance between the global and client models.
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach
We derive an efficient Turbo-variational Bayesian inferencing (Turbo-VBI) algorithm to solve the resulting model compression problem with the proposed prior.
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
This enables client models to be updated in a shared feature space with consistent classifiers during local training.
Bi-directional Digital Twin and Edge Computing in the Metaverse
Multi-access edge computing (MEC) provides responsive services to the end users, ensuring an immersive and interactive Metaverse experience.
6G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions
The Metaverse has emerged as the successor of the conventional mobile internet to change people's lifestyles.
An Efficient Ratio Detector for Ambient Backscatter Communication
Different from original ratio detectors that use the magnitude ratio of the signals between two Reader antennas, in our proposed approach, we utilize the complex ratio so that phase information is preserved and propose an accurate linear channel model approximation.
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning
In this paper, we investigate the joint offloading, communication and computation resource allocation for IRS-assisted NOMA MEC system.
Optimal Regularized Online Allocation by Adaptive Re-Solving
This paper introduces a dual-based algorithm framework for solving the regularized online resource allocation problems, which have potentially non-concave cumulative rewards, hard resource constraints, and a non-separable regularizer.
Distributed Intelligence in Wireless Networks
In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks, with a focus on the basic concepts of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications.
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i. e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i. e., robustness).
Online Network Utility Maximization: Algorithm, Competitive Analysis, and Applications
In addition, since worst-case scenarios rarely occur in practice, we devise an adaptive implementation of our algorithm to improve its average-case performance and validate its effectiveness via simulations.
Data Structures and Algorithms
Enhancing Ambient Backscatter Communication Utilizing Coherent and Non-Coherent Space-Time Codes
To overcome this challenge, we propose the use of orthogonal space-time block codes (OSTBC) by incorporating multiple antennas at the Tag as well as at the Reader.
