  Federated Learning (FL) is a distributed machine learning paradigm
facilitating participants to collaboratively train a model without revealing
their local data. However, when FL is deployed into the wild, some intelligent
clients can deliberately deviate from the standard training process to make the
global model inclined toward their local model, thereby prioritizing their
local data distribution. We refer to this novel category of misbehaving clients
as selfish. In this paper, we propose a Robust aggregation strategy for FL
server to mitigate the effect of Selfishness (in short RFL-Self). RFL-Self
incorporates an innovative method to recover (or estimate) the true updates of
selfish clients from the received ones, leveraging robust statistics (median of
norms) of the updates at every round. By including the recovered updates in
aggregation, our strategy offers strong robustness against selfishness. Our
experimental results, obtained on MNIST and CIFAR-10 datasets, demonstrate that
just 2% of clients behaving selfishly can decrease the accuracy by up to 36%,
and RFL-Self can mitigate that effect without degrading the global model
performance.
