scholarly journals Improved hybrid scheduling design of partitioned processing and network communication in distributed integrated modular avionics systems

2021 ◽  
Author(s):  
Xuan Zhou ◽  
Feng He ◽  
Ershuai Li ◽  
Jianhua Liu ◽  
Huagang Xiong
Keyword(s):  
Network Communication ◽  
Hybrid Scheduling ◽  
Integrated Modular Avionics

Application of CAN bus hybrid scheduling algorithm in HEV control system

2012 ◽  
Vol 26 (1) ◽  
pp. 31-36
Author(s):  
Andong Yin ◽  
Yunxiao Zhu ◽  
Hao Jiang ◽  
Han Zhao
Keyword(s):  
Control System ◽  
Can Bus ◽  
Scheduling Algorithm ◽  
Hybrid Scheduling

scholarly journals Design of Networked Ammunition Network Communication Scheme Based on ESP8266

2021 ◽  
Vol 1748 ◽  
pp. 042030
Author(s):  
Xuhuan Zhang ◽  
Haojie Li ◽  
Hu Huang ◽  
Yue Feng
Keyword(s):  
Network Communication ◽  
Communication Scheme

scholarly journals FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 600
Author(s):  
Sunghwan Park ◽  
Yeryoung Suh ◽  
Jaewoo Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Data Privacy ◽  
Particle Swarm ◽  
Global Model ◽  
Network Communication ◽  
Improve Performance ◽  
Communication Performance ◽  
Swarm Optimization ◽  
Model Update ◽  
Network Environments

Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network.

scholarly journals IoV Scenario: Bandwidth Aware Algorithm in Wireless Network Communication Mode

2020 ◽  
pp. 1-1
Author(s):  
Peiying Zhang ◽  
Gagangeet Singh ◽  
Neeraj Kumar ◽  
Mohsen Guizani
Keyword(s):  
Wireless Network ◽  
Network Communication ◽  
Communication Mode
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