On the Performance Impact of Poisoning Attacks on Load Forecasting in Federated Learning

被引：3

作者：

Qureshi, Naik Bakht Sania ^{[1
]}

Kim, Dong-Hoon ^{[1
]}

Lee, Jiwoo ^{[1
]}

Lee, Eun-Kyu ^{[1
]}

机构：

[1] Incheon Natl Univ, Incheon, South Korea

来源：

UBICOMP/ISWC '21 ADJUNCT: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2021 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS | 2021年

关键词：

Federated Learning; Poisoning Attack; Load Forecasting; Security; Distributed System; Energy Data; Artificial Intelligence;

D O I：

10.1145/3460418.3479285

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article examines a poisoning attack on federated learning. While recent studies are actively exploring this topic in classification models of learning such as image recognition, there are few studies that address the topic in regression models. In particular, this research investigates the impacts of poisoning attacks on the performance of load forecasting, which has hardly studied yet in academia. This research implements two poisoning attacks on a federated learning setting and runs experiments to enumerate their impacts on prediction accuracy of load forecasting. With initial results, we plan to bring a couple of research questions for open discussion to audience.

引用

页码：64 / 66

页数：3