Federated Learning for Assigning Weights to Clients on Long-Tailed Data

Federated learning enables multiple clients to collaboratively train a shared model without transmitting their data. Although this novel approach offers significant advantages in data privacy protection, the variations in data distribution among clients can lead to inconsistencies in model updates, particularly in long-tailed data, which prominently affect the model's ability to learn generalizable features essential for enhancing local model performance. In this study, we propose a novel re-weighting federated learning method, which incorporates a dynamic weight allocation mechanism aimed at balancing the local model updates from each client with the aggregation of the global model during training. Specifically, we employ balanced resampling locally at each client to rectify biases and perform cluster clients based on feature similarity, assigning weights appropriately. This strategy not only strengthens the model's capacity to learn cross-client generalizable features but also minimizes the divergence between local models and the global model. The empirical results on the MNIST-LT and EMNIST-LT datasets demonstrate that our method outperforms baseline approaches, revealing key factors behind its effectiveness.