  As some recent information security legislation endowed users with
unconditional rights to be forgotten by any trained machine learning model,
personalized 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 decentralized learning scenarios. In
this paper, we design a decentralized unlearning framework called HDUS, 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.
