DynFed: Adaptive Federated Learning via Quantization-Aware Knowledge Distillation

Federated Learning (FL) has become a powerful technique for collaborative model training across decentralized entities while preserving data privacy. Despite its potential, FL faces significant challenges, including communication overhead, resource heterogeneity, and data heterogeneity. Existing solutions fall short in addressing disparities in client resources and the errors introduced by direct model aggregation across heterogeneous clients. To tackle these issues, we propose DynFed, a novel federated learning framework that incorporates dynamic quantization bit-width allocation and multi-teacher knowledge distillation for model aggregation. DynFed dynamically adjusts quantization bit-widths to clients based on their resource heterogeneity, adapting these allocations according to variations in the local loss function during training. This adaptive quantization strategy optimizes resource utilization while preserving model performance. For model aggregation, DynFed utilizes a dynamic multi-teacher knowledge distillation approach, assigning the most suitable teacher model to each data sample based on a comprehensive evaluation score, thereby ensuring effective knowledge transfer even in the presence of quantization-induced errors. This method not only mitigates the negative effects of heterogeneous bit-widths but also leverages client model diversity to enhance the robustness of the global model. Extensive experimental results demonstrate the superiority of DynFed over state-of-the-art methods.