  Federated Learning (FL) is a novel approach that allows for collaborative
machine learning while preserving data privacy by leveraging models trained on
decentralized devices. However, FL faces challenges due to non-uniformly
distributed (non-iid) data across clients, which impacts model performance and
its generalization capabilities. To tackle the non-iid issue, recent efforts
have utilized the global model as a teaching mechanism for local models.
However, our pilot study shows that their effectiveness is constrained by
imbalanced data distribution, which induces biases in local models and leads to
a 'local forgetting' phenomenon, where the ability of models to generalize
degrades over time, particularly for underrepresented classes. This paper
introduces FedDistill, a framework enhancing the knowledge transfer from the
global model to local models, focusing on the issue of imbalanced class
distribution. Specifically, FedDistill employs group distillation, segmenting
classes based on their frequency in local datasets to facilitate a focused
distillation process to classes with fewer samples. Additionally, FedDistill
dissects the global model into a feature extractor and a classifier. This
separation empowers local models with more generalized data representation
capabilities and ensures more accurate classification across all classes.
FedDistill mitigates the adverse effects of data imbalance, ensuring that local
models do not forget underrepresented classes but instead become more adept at
recognizing and classifying them accurately. Our comprehensive experiments
demonstrate FedDistill's effectiveness, surpassing existing baselines in
accuracy and convergence speed across several benchmark datasets.
