Anomaly detection using Federated Learning: A Performance Based Parameter Aggregation Approach

Pushkar Bhat*, M. M. Manohara Pai, Radhika M. Pai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Anomaly detection is a significant challenge that exists in multiple industries, including healthcare, finance, and manufacturing. Machine Learning techniques are extensively being used to solve these problems. However, data privacy concerns and regulations are making acquiring the necessary data for model training increasingly challenging, particularly in sensitive sectors. Federated learning has emerged as a promising solution, allowing local models to be trained on edge devices using local datasets and model parameters to be shared with a central server for aggregation. This allows the models to reap the benefits of the distributed data without actually sharing any data. However, the current aggregation method used in federated learning is susceptible to models trained on bad data, which can impact the accuracy and reliability of the resulting model. To address this issue, the study proposes a new filtered aggregation algorithm that greatly mitigates the effect of models trained on bad data. The proposed algorithm accounts for models that cannot detect anomalous patterns and gives them much less representation in the final model, resulting in improved accuracy and reliability.

Original languageEnglish
Title of host publication2023 3rd International Conference on Intelligent Technologies, CONIT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350338607
DOIs
Publication statusPublished - 2023
Event3rd IEEE International Conference on Intelligent Technologies, CONIT 2023 - Hubli, India
Duration: 23-06-202325-06-2023

Publication series

Name2023 3rd International Conference on Intelligent Technologies, CONIT 2023

Conference

Conference3rd IEEE International Conference on Intelligent Technologies, CONIT 2023
Country/TerritoryIndia
CityHubli
Period23-06-2325-06-23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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