国际文摘  

出　　处：《建筑节能（中英文）》2024年第12期52-52,56,共2页Building Energy Efficiency

摘　　要：[OA](1)Centralised vs.decentralised federated load forecasting in smart buildings:Who holds the key to adversarial attack robustness?by Habib UllahManzoor,Sajjad Hussain,David Flynn,et al,Article 114871 Abstract:The integration of AI and ML into energy forecasting is crucial for modern energy management.Federated Learning(FL)is particularly noteworthy because it enhances data privacy and facilitates collaboration among distributed energy resources.It enables model training across multiple locations while minimizing reliance on centralized servers and data transfers.However,FL faces significant security challenges,particularly from adversarial attacks that can undermine the models’integrity and reliability.This paper addresses these security concerns by evaluating the effectiveness of Centralized Federated Learning(CFL)and Decentralized Federated Learning(DFL)in distributed load forecasting.

关 键 词：forecasting holds LOAD 

分 类 号：TU201.5[建筑科学—建筑设计及理论]