  In recent years, Federated Learning (FL) has garnered significant attention
as a distributed machine learning paradigm. To facilitate the implementation of
the right to be forgotten, the concept of federated machine unlearning (FMU)
has also emerged. However, current FMU approaches often involve additional
time-consuming steps and may not offer comprehensive unlearning capabilities,
which renders them less practical in real FL scenarios. In this paper, we
introduce FedAU, an innovative and efficient FMU framework aimed at overcoming
these limitations. Specifically, FedAU incorporates a lightweight auxiliary
unlearning module into the learning process and employs a straightforward
linear operation to facilitate unlearning. This approach eliminates the
requirement for extra time-consuming steps, rendering it well-suited for FL.
Furthermore, FedAU exhibits remarkable versatility. It not only enables
multiple clients to carry out unlearning tasks concurrently but also supports
unlearning at various levels of granularity, including individual data samples,
specific classes, and even at the client level. We conducted extensive
experiments on MNIST, CIFAR10, and CIFAR100 datasets to evaluate the
performance of FedAU. The results demonstrate that FedAU effectively achieves
the desired unlearning effect while maintaining model accuracy.
