PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection

4 June 2024

Papers citing "PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection"

7 / 7 papers shown

Title
Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation Chenxi Liu Hao Miao Qianxiong Xu Shaowen Zhou Cheng Long Yan Zhao Ziyue Li Rui Zhao AI4TS 33 1 0 04 May 2025
Fully Automated Correlated Time Series Forecasting in Minutes Xinle Wu Xingjian Wu Dalin Zhang Miao Zhang Chenjuan Guo Bin Yang Christian S. Jensen AI4TS 22 4 0 06 Nov 2024
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks? Zhongjian Zhang Xiao Wang Huichi Zhou Yue Yu Mengmei Zhang Cheng Yang Chuan Shi AAML 27 7 0 16 Aug 2024
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting Qingxiang Liu Xu Liu Chenghao Liu Qingsong Wen Yuxuan Liang AI4TS AI4CE 32 6 0 23 May 2024
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework Jiayun Zhang Xiyuan Zhang Xinyang Zhang Dezhi Hong Rajesh K. Gupta Jingbo Shang FedML 46 7 0 01 Jan 2023
Improving Fairness for Data Valuation in Horizontal Federated Learning Zhenan Fan Huang Fang Zirui Zhou Jian Pei M. Friedlander Changxin Liu Yong Zhang TDI FedML 19 47 0 19 Sep 2021
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Haoyi Zhou Shanghang Zhang J. Peng Shuai Zhang Jianxin Li Hui Xiong Wan Zhang AI4TS 164 3,799 0 14 Dec 2020