  Federated learning (FL) has recently emerged as a promising distributed
machine learning (ML) paradigm. Practical needs of the "right to be forgotten"
and countering data poisoning attacks call for efficient techniques that can
remove, or unlearn, specific training data from the trained FL model. Existing
unlearning techniques in the context of ML, however, are no longer in effect
for FL, mainly due to the inherent distinction in the way how FL and ML learn
from data. Therefore, how to enable efficient data removal from FL models
remains largely under-explored. In this paper, we take the first step to fill
this gap by presenting FedEraser, the first federated unlearning methodology
that can eliminate the influence of a federated client's data on the global FL
model while significantly reducing the time used for constructing the unlearned
FL model.The basic idea of FedEraser is to trade the central server's storage
for unlearned model's construction time, where FedEraser reconstructs the
unlearned model by leveraging the historical parameter updates of federated
clients that have been retained at the central server during the training
process of FL. A novel calibration method is further developed to calibrate the
retained updates, which are further used to promptly construct the unlearned
model, yielding a significant speed-up to the reconstruction of the unlearned
model while maintaining the model efficacy. Experiments on four realistic
datasets demonstrate the effectiveness of FedEraser, with an expected speed-up
of $4\times$ compared with retraining from the scratch. We envision our work as
an early step in FL towards compliance with legal and ethical criteria in a
fair and transparent manner.
