A Selective Model Aggregation Approach in Federated Learning for Online Anomaly Detection

Yang Qin, Hiroki Matsutani, Masaaki Kondo

研究成果: Conference contribution

抄録

Cloud computing has established a convenient approach for computing offloading, where the data produced by edge devices is gathered and processed in a centralized server. However, it results in critical issues related to latency. Recently, a neural network-based on-device learning approach is proposed, which offers a solution to the latency problem by relocating processing data to edge devices; even so, a single edge device may face insufficient training data to train a high-quality model, because of its limited available processing capabilities and energy resources. To address this issue, we extend the work to a federated learning system which enables the edge devices to exchange their trained parameters and update local models. However, in federated learning for anomaly detection, the reliability of local models would be different. For example, a number of trained models are likely to contain the features of anomalous data because of noise corruption or anomaly detection failure. Besides, as the communication protocol amongst edges could be exploited by attackers, the training data or model weights may have potential risks of being poisoned. Therefore, when we design a federated training algorithm, we should carefully select the local models that participate in model aggregation. In this work, we leverage an observed dataset to compute prediction errors, so that the unsatisfying local models can be excluded from federated training. Experimental results show that the federated learning approach improves anomaly detection accuracy. Besides, the proposed model aggregation solution achieves obvious improvement compared with the popular Federated Averaging method.

本文言語English
ホスト出版物のタイトルProceedings - IEEE Congress on Cybermatics
ホスト出版物のサブタイトル2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ684-691
ページ数8
ISBN(電子版)9781728176475
DOI
出版ステータスPublished - 2020 11
イベント2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020 - Rhodes Island, Greece
継続期間: 2020 11 22020 11 6

出版物シリーズ

名前Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020

Conference

Conference2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020
CountryGreece
CityRhodes Island
Period20/11/220/11/6

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Renewable Energy, Sustainability and the Environment
  • Communication

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