When Federated Learning Meets Machine Unlearning

Authors

  • Mary J Bartra TOMS Bloomberg machine learning and ai lab

DOI:

https://doi.org/10.5281/zenodo.13854241

ARK:

https://n2t.net/ark:/40704/JIEAS.v2n5a07

References:

26

Keywords:

Heterogeneous Machine Learning, Federated Learning, Machine Unlearning, Graph Neural Network, Gradient-based Learning, Elastic Weight Consolidation

Abstract

This research paper explores the intersection of Incremental Learning and Unlearning in the context of machine learning, with a particular emphasis on dynamic environments where data evolves rapidly, and models must continuously adapt. Incremental learning allows machine learning models to efficiently update their knowledge without retraining from scratch, while incremental unlearning ensures compliance with privacy regulations or user requests by selectively removing specific data points from the model. The paper discusses several key techniques for balancing learning and unlearning, including Elastic Weight Consolidation (EWC), gradient-based unlearning, fine-tuning, and memory-based methods.

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Author Biography

Mary J Bartra, TOMS Bloomberg machine learning and ai lab

TOMS Bloomberg machine learning and ai lab, USA.

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Published

2024-10-01

How to Cite

[1]
M. J. Bartra, “When Federated Learning Meets Machine Unlearning”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 5, pp. 39–47, Oct. 2024.

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