FedPS: Model Aggregation with Pseudo Samples

2021 ◽  
pp. 78-88
Author(s):  
Mulin Xu
Keyword(s):  
Model Aggregation

Layered Model Aggregation based Federated Learning in Mobile Edge Networks

2021 ◽  
Author(s):  
Qiming Cao ◽  
Xing Zhang ◽  
Yushun Zhang ◽  
Yongdong Zhu
Keyword(s):  
Layered Model ◽  
Model Aggregation ◽  
Edge Networks

Skeleton-Based Labanotation Generation Using Multi-model Aggregation

2020 ◽  
pp. 554-565
Author(s):  
Ningwei Xie ◽  
Zhenjiang Miao ◽  
Jiaji Wang
Keyword(s):  
Model Aggregation

scholarly journals Federated learning over WiFi: Should we use TCP or UDP?

2021 ◽  
Author(s):  
Vineeth S
Keyword(s):  
Packet Loss ◽  
Distributed Learning ◽  
Learning Paradigm ◽  
Limited Bandwidth ◽  
Tcp Protocol ◽  
Network Connections ◽  
Model Aggregation ◽  
Centralized Model

Federated learning is a distributed learning paradigm where a centralized model is trained on data distributed over a large number of clients, each with unreliable and relatively slow network connections. The client connections typically have limited bandwidth available to them when using networks such as 2G, 3G, or WiFi. As a result, communication often becomes a bottleneck. Currently, the communication between the clients and server is mostly based on TCP protocol. In this paper, we explore using the UDP protocol for the communication between the clients and server. In particular, we develop UDP-based algorithms for gradient aggregation-based federated learning and model aggregation-based federated learning. We propose methods to construct model updates in case of packet loss with the UDP protocol. We present a scalable framework for practical federated learning. We conduct experiments over WiFi and observe that the UDP-based protocols can lead to faster convergence than the TCP-based protocol -- especially in bad networks. Code available at the repository: \url{https://github.com/vineeths96/Federated-Learning}.

scholarly journals Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 425
Author(s):  
Krzysztof Gajowniczek ◽  
Iga Grzegorczyk ◽  
Michał Gostkowski ◽  
Tomasz Ząbkowski
Keyword(s):  
Blind Source Separation ◽  
Cardiac Arrhythmias ◽  
Classification Accuracy ◽  
Recursive Partitioning ◽  
Source Separation ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Physiological Signals ◽  
New Approach ◽  
Model Aggregation

In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7.

scholarly journals Structure-Preservation Model Aggregation for Two-Stage Inverters Based Large-Scale Photovoltaic System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 1824-1839
Author(s):  
Yang Han ◽  
Xiangyang Lin ◽  
Ping Yang ◽  
Lin Xu ◽  
Yan Xu ◽  
...  
Keyword(s):  
Large Scale ◽  
Photovoltaic System ◽  
Two Stage ◽  
Structure Preservation ◽  
Model Aggregation

Wind farms model aggregation using probabilistic clustering

2013 ◽  
Author(s):  
Paula Odete Fernandes ◽  
A^ngela Paula Ferreira
Keyword(s):  
Wind Farms ◽  
Probabilistic Clustering ◽  
Model Aggregation
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