Details

Autor(en) / Beteiligte

• Kaspour, Shamisa
• Yassine, Abdulsalam

Titel

Federated Non-Intrusive Load Monitoring for Smart Homes Utilizing Attention-Based Aggregation

Ist Teil von

• 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 2023, p.208-213

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE/IET Electronic Library

Beschreibungen/Notizen

• Nowadays, Non-Intrusive Load Monitoring (NILM) with Federated Learning (FL) framework has become a growing study towards providing a secure energy disaggregation system in smart homes. This study aims at deploying an attention-based aggregation (FedAtt) approach in FL to emphasize agents' behavioral differences when consuming energy from various appliances. The goal of the proposed technique is to minimize the weighted distance between the parameters of the local model and the global model to better represent each local model's characteristics. In this paper, we examine two different models for NILM: Short Sequence-to-Point (SS2P) and Variational Auto-Encoder (VAE). Our goal is to evaluate the effectiveness of FedAtt. The evaluation of the framework was carried out using the UK-DALE and REFIT datasets. The obtained results were then compared against centralized approaches of the models as well as FedAvg. Our findings show that FedAtt generates comparable results to the centralized model and FedAvg while improving the stability of FL at different values of added noise to local parameters.