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 to \emph{maximize} the local empirical loss. In particular, we formulate the unlearning problem as a constrained maximization problem by restricting to an $\ell_2$-norm ball around a suitably chosen reference model to help retain some knowledge learnt from the other clients' data. This allows the client to use projected gradient descent to perform unlearning. The method does neither require global access to the data used for training nor the history of the parameter updates to be stored by the aggregator (server) or any of the clients. Experiments on the MNIST dataset show that the proposed unlearning method is efficient and effective.

在保护用户隐私的前提下，提出了一种联邦学习模型中去除任意客户数据的方法，即通过执反向梯度下降法使局部经验损失最大化，以解决被遗忘权(DP)的问题，并在 MNIST 数据集上进行了实验验证。

联邦不学习：如何高效删除FL中的客户端?