VADAF: Visualization for Abnormal Client Detection and Analysis in Federated Learning

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-23
Author(s):  
Linhao Meng ◽  
Yating Wei ◽  
Rusheng Pan ◽  
Shuyue Zhou ◽  
Jianwei Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Visual Analytics ◽  
Detection Method ◽  
Training Process ◽  
Local Data ◽  
Privacy And Security ◽  
Potential Client ◽  
Distributed Machine Learning ◽  
Large Corpus

Federated Learning (FL) provides a powerful solution to distributed machine learning on a large corpus of decentralized data. It ensures privacy and security by performing computation on devices (which we refer to as clients) based on local data to improve the shared global model. However, the inaccessibility of the data and the invisibility of the computation make it challenging to interpret and analyze the training process, especially to distinguish potential client anomalies. Identifying these anomalies can help experts diagnose and improve FL models. For this reason, we propose a visual analytics system, VADAF, to depict the training dynamics and facilitate analyzing potential client anomalies. Specifically, we design a visualization scheme that supports massive training dynamics in the FL environment. Moreover, we introduce an anomaly detection method to detect potential client anomalies, which are further analyzed based on both the client model’s visual and objective estimation. Three case studies have demonstrated the effectiveness of our system in understanding the FL training process and supporting abnormal client detection and analysis.

scholarly journals Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

2020 ◽  
Vol 69 (8) ◽  
pp. 8459-8467 ◽  
Author(s):  
Yutao Lu ◽  
Juan Wang ◽  
Miao Liu ◽  
Kaixuan Zhang ◽  
Guan Gui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Detection Method ◽  
Supervised Machine Learning

Trust-based federated learning for network anomaly detection

2021 ◽  
pp. 1-11
Author(s):  
Naiyue Chen ◽  
Yi Jin ◽  
Yinglong Li ◽  
Luxin Cai
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Private Information ◽  
Information Disclosure ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Local Data ◽  
Daily Work ◽  
Network Anomaly Detection

With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.

scholarly journals Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 884 ◽  
Author(s):  
Zizheng Zhang ◽  
Shigemi Ishida ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Method ◽  
Detection System ◽  
Wearable Sensors ◽  
Low Frequency ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Features ◽  
Pose Detection

A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario.

scholarly journals XFinder: Detecting Unknown Anomalies in Distributed Machine Learning Scenario

2021 ◽  
Vol 3 ◽  
Author(s):  
Haizhou Du ◽  
Shiwei Wang ◽  
Huan Huo
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Real Time ◽  
Network Traffic ◽  
Security And Privacy ◽  
Closed Sets ◽  
Online Mode ◽  
Average Accuracy ◽  
Learning Scenarios ◽  
Distributed Machine Learning

In recent years, the emergence of distributed machine learning has enabled deep learning models to ensure data security and privacy while training efficiently. Anomaly detection for network traffic in distributed machine learning scenarios is of great significance for network security. Although deep neural networks have made remarkable achievements in anomaly detection for network traffic, they mainly focus on closed sets, that is, assuming that all anomalies are known. However, in a real network environment, unknown abnormalities are fatal risks faced by the system because they have no labels and occur before the known anomalies. In this study, we design and implement XFinder, a dynamic unknown traffic anomaly detection framework in distributed machine learning. XFinder adopts an online mode to detect unknown anomalies in real-time. XFinder detects unknown anomalies by the unknowns detector, transfers the unknown anomalies to the prior knowledge base by the network updater, and adopts the online mode to report new anomalies in real-time. The experimental results show that the average accuracy of the unknown anomaly detection of our model is increased by 27% and the average F1-Score is improved by 20%. Compared with the offline mode, XFinder’s detection time is reduced by an average of approximately 33% on three datasets, and can better meet the network requirement.

An online anomaly detection method for stream data using isolation principle and statistic histogram

2015 ◽  
Vol 06 (02) ◽  
pp. 1550017 ◽  
Author(s):  
Zhiguo Ding ◽  
Minrui Fei ◽  
Dajun Du
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Ensemble Learning ◽  
Detection Method ◽  
Dynamic Evolution ◽  
Stream Data ◽  
Massive Datasets ◽  
Online Anomaly Detection

Online anomaly detection for stream data has been explored recently, where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation. However, due to the inherent complex characteristics of stream data, such as quick generation, tremendous volume and dynamic evolution distribution, how to develop an effective online anomaly detection method is a challenge. The main objective of this paper is to propose an adaptive online anomaly detection method for stream data. This is achieved by combining isolation principle with online ensemble learning, which is then optimized by statistic histogram. Three main algorithms are developed, i.e., online detector building algorithm, anomaly detecting algorithm and adaptive detector updating algorithm. To evaluate our proposed method, four massive datasets from the UCI machine learning repository recorded from real events were adopted. Extensive simulations based on these datasets show that our method is effective and robust against different scenarios.

scholarly journals Federated Learning Versus Classical Machine Learning: A Convergence Comparison

2021 ◽  
Author(s):  
Muhammad Asad ◽  
Ahmed Moustafa ◽  
Takayuki Ito
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Sensitive Information ◽  
Distributed Data ◽  
Training Process ◽  
Local Data ◽  
Training Models ◽  
Privacy Threats ◽  
The Past ◽  
Cloud Server

In the past few decades, machine learning has revolutionized data processing for large scale applications. Simultaneously , increasing privacy threats in trending applications led to the redesign of classical data training models. In particular, classical machine learning involves centralized data training, where the data is gathered, and the entire training process executes at the central server. Despite significant convergence, this training involves several privacy threats on participants’ data when shared with the central cloud server. To this end, federated learning has achieved significant importance over distributed data training. In particular, the federated learning allows participants to collaboratively train the local models on local data without revealing their sensitive information to the central cloud server. In this paper, we perform a convergence comparison between classical machine learning and federated learning on two publicly available datasets, namely, logistic-regression-MNIST dataset and image-classification-CIFAR-10 dataset. The simulation results demonstrate that federated learning achieves higher convergence within limited communication rounds while maintaining participants’ anonymity. We hope that this research will show the benefits and help federated learning to be implemented widely.

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