Blockchain-Based Clustered Federated Learning for Non-Intrusive Load Monitoring

Abstract

To address privacy concerns of state-of-the-art centralized machine learning in non-intrusive load monitoring (NILM) applications, the adoption of federated learning (FL) has emerged as a solution to transfer training processes from cloud servers to edge devices. Nevertheless, conventional FL encounters several challenges including architecture safety, incentive mechanism, computing cost, and personalization. To overcome these challenges, the study proposes a self-motivated decentralized FL scheme for NILM, named blockchain-based clustered FL, by combining blockchain mechanism with clustered FL, incentivizing suitable clients to participate in FL by offering rewards based on data size and model performance. Under NILM-related differential privacy protections, the Laplace noise is injected into the first layer of neural networks in the blockchain-based clustered FL, and a decay factor is employed to mitigate the adverse effects of excessive noise on performance. Lightweight training techniques such as data quantization and weight pruning are employed to reduce computational complexity. Furthermore, a clustering approach is utilized to create multiple global models, thereby enhancing the model personalization degree. It is verified by the case study that the blockchain-based clustered FL outperforms the conventional FL in both accuracy and operation risk, and offers much superior performance and a more robust model compared to local training.