A Trustworthy Decentralized Federated Learning Framework for Consumer Electronics: Mitigating Large-Scale AIoT Heterogeneity Through Transfer Knowledge Integration

IoT-enabled consumer electronics can collect and analyze data to improve functionality and user experiences, increasingly becoming part of edge computing networks. Decentralized federated learning is envisioned as a promising AIoT framework to leverage the data generated by these interconnected consumer electronics without compromising user privacy, where computations are performed closer to the data source rather than in a centralized cloud-based data center. However, a number of obstacles bottleneck the deployment of decentralized learning frameworks under a large-scale AIoT scenario, such as heterogeneity issues, communication efficiency, large-scale issues and others. Specifically, heterogeneity issues include diverse learning models, non-i.i.d. data distribution and unbalanced datasets. In this paper, we propose a trustworthy and adaptive knowledge distillation based decentralized federated learning framework to overcome a number of heterogeneity and large-scale issues. To achieve this goal, we formulate a decentralized optimization problem which formally presents various heterogeneity issues. The proposed framework is composed of two components, including a knowledge distillation based algorithm to transfer knowledge over diverse learning models as well as a real-time evaluation module to adapt client drift issues. Convergence analysis demonstrates that the convergence of the proposed framework is guaranteed. Finally, we conduct extensive experiments over three public datasets to validate the effectiveness and efficiency of proposed framework in the large-scale IoT scenario, illustrating that the proposed framework could significantly improve the learning performance over various metrics and outperforming state-of-the-art baseline approaches.