ORCID Profile
0000-0002-4753-8161
Current Organisation
University of Southern Queensland
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Publisher: IEEE
Date: 11-2011
Publisher: Wiley
Date: 23-09-2020
DOI: 10.1002/CPE.6002
Abstract: Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.
Publisher: Springer Singapore
Date: 2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2013
Publisher: IEEE
Date: 05-2017
Publisher: IEEE
Date: 07-2019
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 18-07-2022
Publisher: ACM Press
Date: 2019
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 11-2018
Publisher: Wiley
Date: 23-06-2014
DOI: 10.1002/SEC.1037
Publisher: Springer Singapore
Date: 2020
Publisher: Wiley
Date: 29-06-2020
DOI: 10.1002/CPE.5906
Abstract: In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants jointly train a global model without uploading their private training data to a third party server. Nevertheless, federated learning is under the threat of poisoning attacks, where adversaries can upload malicious model updates to contaminate the global model. To detect and mitigate poisoning attacks in federated learning, we propose a poisoning defense mechanism, which uses generative adversarial networks to generate auditing data in the training procedure and removes adversaries by auditing their model accuracy. Experiments conducted on two well‐known datasets, MNIST and Fashion‐MNIST, suggest that federated learning is vulnerable to the poisoning attack, and the proposed defense method can detect and mitigate the poisoning attack.
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 06-2023
Publisher: IEEE
Date: 18-07-2022
Publisher: IEEE
Date: 08-2019
Publisher: IEEE
Date: 07-2019
Publisher: MDPI AG
Date: 17-08-2019
DOI: 10.3390/INFO10080262
Abstract: Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
Publisher: IEEE
Date: 12-2021
No related grants have been discovered for Di Wu.