Search Results for author:
Found 25 papers, 1 papers with code
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training, and it delivers comparable performance as that of quantized models tailored to any specific bit-width.
Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy.
In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks
We further propose a virtualized 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
Realizing Ultra-Fast and Energy-Efficient Baseband Processing Using Analogue Resistive Switching Memory
To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient (UFEE) baseband processors.
Accelerating Federated Edge Learning via Topology Optimization
In this paper, a novel topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency.
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL).
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices
To tackle the challenge, in this paper, a federated dropout (FedDrop) scheme is proposed building on the classic dropout scheme for random model pruning.
Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis
The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA.
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Then, we present a detailed literature review on the use of communication techniques for its efficient deployment.
An Energy-efficient Aerial Backhaul System with Reconfigurable Intelligent Surface
In this paper, we propose a novel wireless architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS).
Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer
To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point).
Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks
The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks.
Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels
Targeting broadband channels, we consider the joint control of parameter allocation, sub-channel allocation, and transmission power to improve the performance of PARTEL.
Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing
Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation.
Information Theory Signal Processing Information Theory
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates.
Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning
To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets.
One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme.
Information Theory Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Signal Processing Information Theory
An Introduction to Communication Efficient Edge Machine Learning
The main theme in the area is to design new communication techniques and protocols for efficient implementation of different distributed learning frameworks (i. e., federated learning) in wireless networks.
Information Theory Signal Processing Information Theory
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning
Specifically, the metrics that measure data importance in active learning (e. g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively.
Energy-Efficient Radio Resource Allocation for Federated Edge Learning
To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling.
Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)
To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users.
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning.
Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning
We hope to inspire practitioners in different fields to adopt the powerful tool of Grassmannian learning in their research.
