  Federated learning aims to collaboratively train models without accessing
their client's local private data. The data may be Non-IID for different
clients and thus resulting in poor performance. Recently, personalized
federated learning (PFL) has achieved great success in handling Non-IID data by
enforcing regularization in local optimization or improving the model
aggregation scheme on the server. However, most of the PFL approaches do not
take into account the unfair competition issue caused by the imbalanced data
distribution and lack of positive samples for some classes in each client. To
address this issue, we propose a novel and generic PFL framework termed
Federated Averaging via Binary Classification, dubbed FedABC. In particular, we
adopt the ``one-vs-all'' training strategy in each client to alleviate the
unfair competition between classes by constructing a personalized binary
classification problem for each class. This may aggravate the class imbalance
challenge and thus a novel personalized binary classification loss that
incorporates both the under-sampling and hard sample mining strategies is
designed. Extensive experiments are conducted on two popular datasets under
different settings, and the results demonstrate that our FedABC can
significantly outperform the existing counterparts.
