AccDFL: Accelerated Decentralized Federated Learning for Healthcare IoT Networks

In Healthcare Internet of Things Networks (HIoTNs), safeguarding the sensitivity of patients’ electric healthcare records (EHRs) is imperative, necessitating effective privacy protection while ensuring ample data for training. Decentralized federated learning (DFL), as a peer-to-peer distributed machine learning architecture, serves a pivotal role in preserving the data privacy of EHRs. However, two primary challenges in applying DFL to HIoTNs include safeguarding the privacy of EHRs at individual hospitals and optimizing communication resources among hospitals. This article proposes a three-layer healthcare framework comprising a physical layer, a communication layer and an edge-encrypted layer to segregate hospital local data from interaction models. Moreover, a novel doubly accelerated DFL algorithm (AccDFL) is introduced to integrate DFL training and information interaction into the communication layer of HIoTNs. By employing the heavy-ball and Nesterov methods, AccDFL achieves double acceleration, ensuring both $\epsilon _{i}$ -differential privacy (DP) and linear convergence. The theoretical analysis of AccDFL delves into the interaction among distributed gradient tracking (DGT), DP, and accelerated mechanisms, presenting comprehensive convergence and privacy analyses to overcome the exponential increase in parameters brought by the acceleration and DP mechanisms. Experimental results with realistic non-IID grayscale and color medical datasets of different disease types affirm the significant advantages of AccDFL over other algorithms in terms of accuracy and communication efficiency.