Continual Learning and Private Unlearning
This addresses privacy concerns for users of lifelong autonomous agents, though it is an incremental step in the broader field of machine unlearning.
The paper tackles the problem of enabling intelligent agents to forget specific tasks privately without degrading other learned knowledge, formalizing it as the continual learning and private unlearning (CLPU) problem, and introduces a solution called CLPU-DER++ along with benchmark problems for evaluation.
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.