  Federated learning (FL) is a distributed machine learning paradigm that needs
collaboration between a server and a series of clients with decentralized data.
To make FL effective in real-world applications, existing work devotes to
improving the modeling of decentralized data with non-independent and identical
distributions (non-IID). In non-IID settings, there are intra-client
inconsistency that comes from the imbalanced data modeling, and inter-client
inconsistency among heterogeneous client distributions, which not only hinders
sufficient representation of the minority data, but also brings discrepant
model deviations. However, previous work overlooks to tackle the above two
coupling inconsistencies together. In this work, we propose FedRANE, which
consists of two main modules, i.e., local relational augmentation (LRA) and
global Nash equilibrium (GNE), to resolve intra- and inter-client inconsistency
simultaneously. Specifically, in each client, LRA mines the similarity
relations among different data samples and enhances the minority sample
representations with their neighbors using attentive message passing. In
server, GNE reaches an agreement among inconsistent and discrepant model
deviations from clients to server, which encourages the global model to update
in the direction of global optimum without breaking down the clients
optimization toward their local optimums. We conduct extensive experiments on
four benchmark datasets to show the superiority of FedRANE in enhancing the
performance of FL with non-IID data.
