论文标题
边缘的实时分散知识转移
Real-Time Decentralized knowledge Transfer at the Edge
论文作者
论文摘要
边缘网络的扩散创造了在当地数据流中工作的学习代理岛。实时传输这些代理之间的知识而不曝光私人数据可以使协作减少学习时间并增加模型信心。将本地模型看不到的数据中纳入知识，可以创造出可以使本地模型或在从未见过的数据上添加分类能力的能力。在选择性分散的方法中转移知识使模型能够保留其本地见解，从而允许机器学习模型的本地风味。这种方法适合边缘网络的分散体系结构，因为本地边缘节点将为可能遇到类似数据的学习代理社区服务。我们提出了一种基于知识蒸馏的方法，用于从非i.i.d训练的模型中成对知识转移管道。数据并将其与其他流行的知识转移方法进行比较。此外，我们测试了知识转移网络构建的不同方案，并显示了我们方法的实用性。我们的实验在实时传输方案中使用模型表现出优于标准方法的知识传输。
The proliferation of edge networks creates islands of learning agents working on local streams of data. Transferring knowledge between these agents in real-time without exposing private data allows for collaboration to decrease learning time and increase model confidence. Incorporating knowledge from data that a local model did not see creates an ability to debias a local model or add to classification abilities on data never before seen. Transferring knowledge in a selective decentralized approach enables models to retain their local insights, allowing for local flavors of a machine learning model. This approach suits the decentralized architecture of edge networks, as a local edge node will serve a community of learning agents that will likely encounter similar data. We propose a method based on knowledge distillation for pairwise knowledge transfer pipelines from models trained on non-i.i.d. data and compare it to other popular knowledge transfer methods. Additionally, we test different scenarios of knowledge transfer network construction and show the practicality of our approach. Our experiments show knowledge transfer using our model outperforms standard methods in a real-time transfer scenario.
