As intelligent agents become autonomous over longer periods of time, they may eventually become lifelong counterparts to specific people. If so, it may be common for a user to want the agent to master a task temporarily but later on to forget the task due to privacy concerns. However enabling an agent to \emph{forget privately} what the user specified without degrading the rest of the learned knowledge is a challenging problem. With the aim of addressing this challenge, this paper formalizes this continual learning and private unlearning (CLPU) problem. The paper further introduces a straightforward but exactly private solution, CLPU-DER++, as the first step towards solving the CLPU problem, along with a set of carefully designed benchmark problems to evaluate the effectiveness of the proposed solution. The code is available at https://github.com/Cranial-XIX/Continual-Learning-Private-Unlearning.
Continual Learning and Private Unlearning
The paper addresses the challenge of enabling intelligent agents to remember new tasks while being able to privately forget them without losing previously learned knowledge, introducing a solution called CLPU-DER++ and evaluating it with benchmark problems.
- Year
- 2022
- Venue
- arXiv 2022
- Authors
- 3
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- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/2203.12817v2ARXIV-DEFAULT
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