Affiliation:
1. School of Information Technology and Electrical Engineering The University of Queensland St Lucia Queensland Australia
2. School of Information and Communication Technology Griffith University Brisbane Queensland Australia
Abstract
AbstractAs some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalised IoT service providers have to put unlearning functionality into their consideration. The most straightforward method to unlearn users' contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralised learning scenarios. A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed (HDUS) is designed, which uses distilled seed models to construct erasable ensembles for all clients. Moreover, the framework is compatible with heterogeneous on‐device models, representing stronger scalability in real‐world applications. Extensive experiments on three real‐world datasets show that our HDUS achieves state‐of‐the‐art performance.
Funder
Australian Research Council
Publisher
Institution of Engineering and Technology (IET)
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Information Systems