WebFLAME: Taming Backdoors in Federated Learning Thien Duc Nguyen * , Phillip Rieger, Huili Chen, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal , … WebFederated learning (FL) enables learning a global machine learning model from data distributed among a set of participating workers. This makes it possible (i) to train more accurate models due to learning from rich, joint training data and (ii) to improve privacy by not sharing the workers’ local private data with others.

[2101.02281] FLAME: Taming Backdoors in Federated Learning - arXiv.org

WebOur evaluation of FLAME on several datasets stemming from application areas including image classification, word prediction, and IoT intrusion detection demonstrates that … WebFederated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model with-out having to share their private, potentially sensitive local … sash construction definition

ORCID

WebJan 3, 2024 · Federated Learning (FL) allows multiple clients to collaboratively train a Neural Network (NN) model on their private data without revealing the data. Recently, several targeted poisoning attacks against FL have been introduced. These attacks inject a backdoor into the resulting model that allows adversary-controlled inputs to be … WebSep 17, 2024 · FLAME: Differentially Private Federated Learning in the Shuffle Model Ruixuan Liu, Yang Cao, Hong Chen, Ruoyang Guo, Masatoshi Yoshikawa Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. WebUSENIX Security '22 - FLAME: Taming Backdoors in Federated LearningThien Duc Nguyen and Phillip Rieger, Technical University of Darmstadt; Huili Chen, Univer... AboutPressCopyrightContact... sash continuing education