The Brainy Student: Scalable Unlearning by Selectively Disobeying the Teacher

Meghdad Kurmanji, Peter Triantafillou, Eleni Triantafillou

Keywords: deep machine unlearning, machine unlearning, scalable unlearning

TL;DR: We propose a new approach for deep machine unlearning that breaks free of limiting assumptions made in previous work, scales significantly better and consistently outperforms previous methods across a wide range of scenarios

Abstract: Deep machine unlearning is the problem of removing the influence of a cohort of data from the weights of a trained deep model. This challenge has enjoyed increasing attention recently, motivated to the widespread use of neural networks in applications involving user data: allowing users to exercise their `right to be forgotten' necessitates an effective unlearning algorithm. Deleting data from models is also of interest in practice for removing out-of-date examples, outliers or noisy labels. However, most previous unlearning methods consider simple scenarios where a theoretical treatment is possible. Consequently, not only do their guarantees not apply to deep neural networks, but they also scale poorly. In this paper, drawing inspiration from teacher-student methods, we propose a scalable deep unlearning method that breaks free of previous limiting assumptions. Our thorough empirical investigation reveals that our approach significantly improves upon previous methods in being by far the most consistent in achieving unlearning in a wide range of scenarios, while incurring only a minimal performance degradation, if any, and being significantly more scalable than previous methods.

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