Search Results for author:
Found 19 papers, 3 papers with code
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning
The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity.
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
In this paper, we propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift.
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Newton-type methods are popular in federated learning due to their fast convergence.
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels
In this paper, we propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.
Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation
Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited computational power.
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent
In this paper, we propose an energy-efficient federated meta-learning framework.
Cross Layer Optimization and Distributed Reinforcement Learning Approach for Tile-Based 360 Degree Wireless Video Streaming
Wirelessly streaming high quality 360 degree videos is still a challenging problem.
BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization
This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner.
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth.
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases.
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM).
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper.
Distilling On-Device Intelligence at the Network Edge
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML).
A Proximal Jacobian ADMM Approach for Fast Massive MIMO Signal Detection in Low-Latency Communications
We introduce an objective function that is a sum of strictly convex and separable functions based on decomposing the received vector into multiple vectors.
Information Theory Information Theory
FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP
For example, on an experiment conducted over 100 real cellular bandwidth traces of a public dataset that spans different bandwidth regimes, our proposed algorithm (FastScan) achieves the minimum re-buffering (stall) time and the maximum average playback rate in every single trace as compared to the original dash. js rate adaptation scheme, Festive, BBA, RB, and FastMPC algorithms.
Networking and Internet Architecture Multimedia
LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming
The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches.
Networking and Internet Architecture Multimedia
