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Smart city article details
| Title | Fedratrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated In Smart Grids |
|---|---|
| ID_Doc | 26399 |
| Authors | Al-Quraan M.; Khan A.; Centeno A.; Zoha A.; Imran M.A.; Mohjazi L. |
| Year | 2023 |
| Published | Engineering Applications of Artificial Intelligence, 124 |
| DOI | http://dx.doi.org/10.1016/j.engappai.2023.106654 |
| Abstract | Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources. © 2023 The Author(s) |
| Author Keywords | Energy forecasting; Ensemble learning; Federated averaging; Federated learning; Smart city; Smart grids |
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