With the growing computational capabilities of microcontroller units (MCUs), edge devices can now support machine learning models. However, deploying decentralised federated learning (DFL) on such devices presents key challenges, including intermittent connectivity, limited communication range, and dynamic network topologies. This paper proposes a novel framework, bilayer Gossip Decentralised Parallel Stochastic Gradient Descent (GD PSGD), designed to address these issues in resource-constrained environments. The framework incorporates a hierarchical communication structure using Distributed Kmeans (DKmeans) clustering for geographic grouping and a gossip protocol for efficient model aggregation across two layers: intra-cluster and inter-cluster. We evaluate the framework's performance against the Centralised Federated Learning (CFL) baseline using the MCUNet model on the CIFAR-10 dataset under IID and Non-IID conditions. Results demonstrate that the proposed method achieves comparable accuracy to CFL on IID datasets, requiring only 1.8 additional rounds for convergence. On Non-IID datasets, the accuracy loss remains under 8\% for moderate data imbalance. These findings highlight the framework's potential to support scalable and privacy-preserving learning on edge devices with minimal performance trade-offs.

本研究解决了在微控制器单元（MCU）上部署去中心化联邦学习（DFL）所面临的间歇性连接和网络拓扑动态等挑战。提出了一种名为双层闲聊去中心化并行随机梯度下降（GD PSGD）的新框架，通过分层通信结构与高效模型聚合方法来实现，在CIFAR-10数据集上验证了其与中心化联邦学习（CFL）相当的准确性，展示了其在资源受限环境中可扩展的学习潜力。

TinyML中的去中心化资源共享：无线双层闲聊并行SGD协作学习