Subspace based Federated Unlearning. (arXiv:2302.12448v1 [cs.LG])

Federated learning (FL) enables multiple clients to train a machine learning
model collaboratively without exchanging their local data. Federated unlearning
is an inverse FL process that aims to remove a specified target client's
contribution in FL to satisfy the user's right to be forgotten. Most existing
federated unlearning algorithms require the server to store the history of the
parameter updates, which is not applicable in scenarios where the server
storage resource is constrained. In this paper, we propose a
simple-yet-effective …

algorithms client clients data federated learning history local machine machine learning parameter process remove right to be forgotten server storage store target train updates