Resource-aware Federated Learning using Knowledge Extraction and Multi-model Fusion. (arXiv:2208.07978v1 [cs.DC])

With increasing concern about user data privacy, federated learning (FL) has
been developed as a unique training paradigm for training machine learning
models on edge devices without access to sensitive data. Traditional FL and
existing methods directly employ aggregation methods on all edges of the same
models and training devices for a cloud server. Although these methods protect
data privacy, they are not capable of model heterogeneity, even ignore the
heterogeneous computing power, and incur steep communication costs. In this …

dc federated learning fusion