Federated Unlearning: How to Efficiently Erase a Client in FL?. (arXiv:2207.05521v1 [cs.LG])

With privacy legislation empowering users with the right to be forgotten, it
has become essential to make a model forget about some of its training data. We
explore the problem of removing any client's contribution in federated learning
(FL). During FL rounds, each client performs local training to learn a model
that minimizes the empirical loss on their private data. We propose to perform
unlearning at the client (to be erased) by reversing the learning process,
i.e., training a model …

client lg