Federated learning with class imbalance reduction

Understand

Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model.

• Due to privacy concerns, the raw data on devices could not be available for centralized server.
• Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged to train and transmit the trained model to centralized server for aggregation.
• Since the local data distribution varies among all devices, class imbalance problem arises along with the unfavorable client selection, resulting in a slow converge rate of the global model.