arXiv:2301.09165
공식 코드 · Papers with Code
Energy Prediction using Federated Learning
원문 초록
In this work, we demonstrate the viability of using federated learning to successfully predict energy consumption as well as solar production for all households within a certain network using low-power and low-space consuming embedded devices. We also demonstrate our prediction performance improving over time without the need for sharing private consumer energy data. We simulate a system with four nodes using data for one year to show this.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
토론
이 논문과 연결된 포럼 글이 아직 없습니다.