  Recently, federated learning has emerged as a promising approach for training
a global model using data from multiple organizations without leaking their raw
data. Nevertheless, directly applying federated learning to real-world tasks
faces two challenges: (1) heterogeneity in the data among different
organizations; and (2) data noises inside individual organizations.
  In this paper, we propose a general framework to solve the above two
challenges simultaneously. Specifically, we propose using distributionally
robust optimization to mitigate the negative effects caused by data
heterogeneity paradigm to sample clients based on a learnable distribution at
each iteration. Additionally, we observe that this optimization paradigm is
easily affected by data noises inside local clients, which has a significant
performance degradation in terms of global model prediction accuracy. To solve
this problem, we propose to incorporate mixup techniques into the local
training process of federated learning. We further provide comprehensive
theoretical analysis including robustness analysis, convergence analysis, and
generalization ability. Furthermore, we conduct empirical studies across
different drug discovery tasks, such as ADMET property prediction and
drug-target affinity prediction.
