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Found 16 papers, 1 papers with code
HawkRover: An Autonomous mmWave Vehicular Communication Testbed with Multi-sensor Fusion and Deep Learning
Connected and automated vehicles (CAVs) have become a transformative technology that can change our daily life.
Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges
Channel modeling is fundamental in advancing wireless systems and has thus attracted considerable research focus.
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
In this paper, we address this problem by adapting the aggregation weights in federated averaging (FedAvg) based on the participation history of each client.
Federated Learning with Flexible Control
In this paper, we address this problem and propose FlexFL - an FL algorithm with multiple options that can be adjusted flexibly.
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency.
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions.
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding
To address the heterogeneous communication throughput problem, each full-width (1. 0x) SNN model and its half-width ($0. 5$x) model are superposition-coded before transmission, and successively decoded after reception as the 0. 5x or $1. 0$x model depending on the channel quality.
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks
The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee.
Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing
Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols.
Information Theory Networking and Internet Architecture Information Theory
A New Design of Cache-aided Multiuser Private Information Retrieval with Uncoded Prefetching
This paper considers the MuPIR problem with two messages, arbitrary number of users and databases where uncoded prefetching is assumed, i. e., the users directly copy some bits from the library as their cached contents.
On Secure Distributed Linearly Separable Computation
Then we focus on the case where the computation cost of each server is minimum and aim to minimize the size of the randomness variable introduced in the system while achieving the optimal communication cost.
Information Theory Information Theory
Cache-aided General Linear Function Retrieval
Coded Caching, proposed by Maddah-Ali and Niesen (MAN), has the potential to reduce network traffic by pre-storing content in the users' local memories when the network is underutilized and transmitting coded multicast messages that simultaneously benefit many users at once during peak-hour times.
Information Theory Information Theory
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD
Furthermore, we extend our analytical approach based on "upward" and "downward" divergences to study the convergence for the general case of H-SGD with more than two levels, where the "sandwich behavior" still holds.
On the Fundamental Limits of Cache-aided Multiuser Private Information Retrieval
Based on the proposed novel approach of \emph{cache-aided interference alignment (CIA)}, first, for the MuPIR problem with $K=2$ messages, $K_{\rm u}=2$ users and $N\ge 2$ databases, we propose achievable retrieval schemes for both uncoded and general cache placement.
