What question did this study set out to answer?

The aim is to explore machine unlearning methods and propose a unified framework to address privacy and accountability.

February 6, 2026Open Access

Machine Unlearning : A Comprehensive Survey and Unified Framework

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The aim is to explore machine unlearning methods and propose a unified framework to address privacy and accountability.
Conducted a survey of existing machine unlearning techniques
Organized methods into a taxonomy including exact and approximate unlearning
Analyzed verification and evaluation approaches across different scenarios
Identified application requirements for real-world implementation
Introduced a unified framework for integrating unlearning techniques with verification strategies
Emphasized the need for standardized metrics in evaluating unlearning effectiveness
Highlighted the trade-offs between guarantees, efficiency, and deployability in machine unlearning systems
Outlined potential deployment strategies, such as federated learning and large language models

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This comprehensive survey reviews machine unlearning methods and frameworks for privacy compliance, indicating their importance in modern AI systems.

Venkata Krishnaveni Chennuru (2026) studied this question.

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This comprehensive survey reviews machine unlearning methods and frameworks for privacy compliance, indicating their importance in modern AI systems.