A Decentralized Federated Learning Based on Node Selection and Knowledge Distillation

Abstract

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. Existing federated learning mainly adopts a central server-based network topology, however, the training process of which is susceptible to the central node. To address this problem, this article proposed a decentralized federated learning method based on node selection and knowledge distillation. Specifically, the central node in this method is variable, and it is selected by the indicator interaction between nodes. Meanwhile, the knowledge distillation mechanism is added to make the student model as close as possible to the teacher’s network and ensure the model’s accuracy. The experiments were conducted on the public MNIST, CIFAR-10, and FEMNIST datasets for both the Independent Identically Distribution (IID) setting and the non-IID setting. Numerical results show that the proposed method can achieve an improved accuracy as compared to the centralized federated learning method, and the computing time is reduced greatly with less accuracy loss as compared to the blockchain decentralized federated learning. Therefore, the proposed method guarantees the model effect while meeting the individual model requirements of each node and reducing the running time.

Aggregation Methods Based on Quality Model Assessment for Federated Learning Applications: Overview and Comparative Analysis

“…Depending on the communication allowed between nodes, FL methods can be grouped into centralized and decentralized methods [14]. Centralized federated learning (CFL) is a commonly used architecture that includes a server and allows client-server interactions [1,15], while the decentralized federated learning (DFL) structure does not contain a centralized node and allows the clients to communicate directly with each other [16]. In this paper, the focus is on the HFL and CFL structure, which is also named sample-based federated learning.…”