  Concerned with user data privacy, this paper presents a new federated
learning (FL) method that trains machine learning models on edge devices
without accessing sensitive data. Traditional FL methods, although
privacy-protective, fail to manage model heterogeneity and incur high
communication costs due to their reliance on aggregation methods. To address
this limitation, we propose a resource-aware FL method that aggregates local
knowledge from edge models and distills it into robust global knowledge through
knowledge distillation. This method allows efficient multi-model knowledge
fusion and the deployment of resource-aware models while preserving model
heterogeneity. Our method improves communication cost and performance in
heterogeneous data and models compared to existing FL algorithms. Notably, it
reduces the communication cost of ResNet-32 by up to 50\% and VGG-11 by up to
10$\times$ while delivering superior performance.
