A Secure Aggregation for Federated Learning on Long-Tailed Data
Yanna Jiang Baihe Ma Xu Wang Guangsheng Yu Caijun Sun Wei Ni Ren Ping Liu
As a distributed learning, Federated Learning (FL) faces two challenges: the
unbalanced distribution of training data among participants, and the model
attack by Byzantine nodes. In this paper, we consider the long-tailed
distribution with the presence of Byzantine nodes in the FL scenario. A novel
two-layer aggregation method is proposed for the rejection of malicious models
and the advisable selection of valuable models containing tail class data
information. We introduce the concept of think tank to leverage the wisdom of
all participants. Preliminary experiments validate that the think tank can make
effective model selections for global aggregation.