Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion
This addresses privacy-preserving machine learning for edge devices with heterogeneous data and models, but it is incremental as it builds on existing federated learning methods.
The paper tackles the problem of high communication costs and poor performance in heterogeneous federated learning by proposing a method that aggregates local knowledge and distills it into global knowledge, reducing ResNet-32 communication cost by up to 50% and VGG-11 by up to 10x while improving performance.
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.