| _version_ | 1866901709421281280 |
|---|---|
| author | Duttagupta, Abhishek |
| author_facet | Duttagupta, Abhishek |
| contents | <p>The thesis focuses on simulating a Federated Learning framework using the London Household smart energy meter dataset. The primary objective is to achieve comparable accuracy in load forecasting to state-of-the-art (SOTA) technologies while demonstrating the preservation of data privacy through FL. In real-world scenarios, the challenge of noni.i.d. connections persists. To replicate real-world conditions, limited random connections are considered for each round of FL. Additionally, since smart meters often have limited computational power, small sequential dense neural networks (DNN) are utilized to address this constraint. These DNN models are well-suited for low computational power devices and contribute to solving the computational challenges posed by smart meters. Through this project, the aim is to showcase the potential of FL in maintaining data privacy while achieving accurate load forecasting results, even in resource-constrained environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15375891 |
| institution | Zenodo |
| language | |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Resource constraint Federated Learning for Energy Smart Meters Duttagupta, Abhishek <p>The thesis focuses on simulating a Federated Learning framework using the London Household smart energy meter dataset. The primary objective is to achieve comparable accuracy in load forecasting to state-of-the-art (SOTA) technologies while demonstrating the preservation of data privacy through FL. In real-world scenarios, the challenge of noni.i.d. connections persists. To replicate real-world conditions, limited random connections are considered for each round of FL. Additionally, since smart meters often have limited computational power, small sequential dense neural networks (DNN) are utilized to address this constraint. These DNN models are well-suited for low computational power devices and contribute to solving the computational challenges posed by smart meters. Through this project, the aim is to showcase the potential of FL in maintaining data privacy while achieving accurate load forecasting results, even in resource-constrained environments.</p> |
| title | Resource constraint Federated Learning for Energy Smart Meters |
| url | https://doi.org/10.5281/zenodo.15375891 |