Search Results for author:
Found 15 papers, 6 papers with code
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
In this scheme, vehicle s mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.
Blockchain-Enabled Variational Information Bottleneck for IoT Networks
In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion.
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs.
URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing
Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing.
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing
Furthermore, as vehicles have different local training time due to various sizes of local data and their different computing capabilities, asynchronous federated learning (AFL) is employed to facilitate the RSU to update the global model immediately after receiving a local model to reduce the aggregation delay.
Deep Reinforcement Learning Based Power Allocation for Minimizing AoI and Energy Consumption in MIMO-NOMA IoT Systems
It is critical to determine the optimal policy including sample collection requirements and power allocation to minimize the AoI and energy consumption of MIMO-NOMA IoT system, where the transmission rate is not a constant in the SIC process and the noise is stochastic in the MIMO-NOMA channel.
Asynchronous Federated Learning for Edge-assisted Vehicular Networks
For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model, thus the data privacy can be protected through sharing model parameters instead of data.
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay.
Time-Dependent Performance Modeling for Platooning Communications at Intersection
In this paper, we construct the time-dependent model to evaluate the platooning communication performance at the intersection based on the initial movement characteristics.
Differential Privacy Meets Federated Learning under Communication Constraints
The performance of federated learning systems is bottlenecked by communication costs and training variance.
Delay Sensitive Task Offloading in the 802.11p Based Vehicular Fog Computing Systems
Furthermore, the long-term reward of the system (i. e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers.
Networking and Internet Architecture
Time-dependent Performance Analysis of the 802.11p-based Platooning Communications Under Disturbance
However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i. e., aerodynamics drag and rolling resistance moment etc.
Networking and Internet Architecture
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
Thus, in this paper, we design a novel edge computing-assisted federated learning framework, in which the communication constraints between IoT devices and edge servers and the effect of various IoT devices on the training accuracy are taken into account.
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning
Mobile IoT is composed by mobile IoT devices such as vehicles, wearable devices and smartphones.
Content-Aware User Association and Multi-User MIMO Beamforming over Mobile Edge Caching
In this paper, multiple-input-multiple-output (MIMO) operation and user association policy are linked to the underlying cache placement strategy to ensure a good trade-off between load balancing and backhaul traffic taking into account the underlying wireless channel and the finite cache capacity at edge servers.
