Article
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Synergies between Class Incremental Learning and Machine Unlearning
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Version 1
: Received: 24 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (12:19:55 CEST)
How to cite: Qi, C. Synergies between Class Incremental Learning and Machine Unlearning. Preprints 2024, 2024091992. https://doi.org/10.20944/preprints202409.1992.v1 Qi, C. Synergies between Class Incremental Learning and Machine Unlearning. Preprints 2024, 2024091992. https://doi.org/10.20944/preprints202409.1992.v1
Abstract
The convergence of Class Incremental Learning (CIL) and Machine Unlearning (MU) is a rapidly developing field in machine learning, especially relevant in adaptive and privacy-sensitive environments like finance. CIL enables models to learn new data classes over time without losing previously acquired knowledge, while MU focuses on selectively forgetting specific data to comply with privacy laws or mitigate security risks. In this paper, we examine the theoretical foundations and practical applications of both approaches, particularly in the financial domain. We explore how these two paradigms interact and complement each other, discuss key algorithms, and present examples to illustrate their applications in areas such as portfolio management, fraud detection, and data privacy. Finally, we explore challenges and potential future directions in achieving optimal synergy between CIL and MU.
Keywords
Incremental Learning; Machine Unlearning; Optimization; Graph Neural Network
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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