LoMar: A Local Defense Against Poisoning Attack on Federated Learning

LoMar: A Local Defense Against Poisoning Attack on Federated Learning

8 January 2022

Papers citing "LoMar: A Local Defense Against Poisoning Attack on Federated Learning"

9 / 9 papers shown

Title
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information Yun Xin Jianfeng Lu Shuqin Cao Gang Li Haozhao Wang Guanghui Wen FedML 19 0 0 09 May 2025
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning Naveen Kumar Krishna Mohan Aravind Machiry AAML 29 1 0 05 Apr 2024
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance Wenqi Wei Ling Liu 25 16 0 02 Feb 2024
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach Kai Li Jingjing Zheng Xinnan Yuan W. Ni Ozgur B. Akan H. Vincent Poor AAML 10 15 0 30 Nov 2023
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses M. Ferrag Othmane Friha B. Kantarci Norbert Tihanyi Lucas C. Cordeiro Merouane Debbah Djallel Hamouda Muna Al-Hawawreh K. Choo 23 42 0 17 Jun 2023
Poisoning Attacks and Defenses in Federated Learning: A Survey S. Sagar Chang-Sun Li S. W. Loke Jinho D. Choi OOD FedML 16 9 0 14 Jan 2023
On the Convergence of Multi-Server Federated Learning with Overlapping Area Zhe Qu Xingyu Li Jie Xu Bo Tang Zhuo Lu Yao-Hong Liu FedML 45 14 0 16 Aug 2022
Adversarial Analysis of the Differentially-Private Federated Learning in Cyber-Physical Critical Infrastructures Md Tamjid Hossain S. Badsha Hung M. La Haoting Shen Shafkat Islam Ibrahim Khalil X. Yi AAML 17 3 0 06 Apr 2022
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,032 0 29 Nov 2018