WebFederated learning over distributed multi-party data is an emerging paradigm that iteratively aggregates updates from a group of devices to train a globally shared model. Relying on a set of devices, however, opens up the door for sybil attacks: malicious devices may be controlled by a single adversary who directs these devices to attack the ... WebJan 6, 2024 · Our evaluation of FLAME on several datasets stemming from application areas including image classification, word prediction, and IoT intrusion detection …

FLAME: Taming Backdoors in Federated Learning

WebJul 2, 2024 · An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task. We evaluate the attack under different assumptions for the standard federated-learning tasks and show that it greatly outperforms data poisoning. WebOur evaluation of FLAME on several datasets stemming from application areas including image classification, word prediction, and IoT intrusion detection demonstrates that … phoenix flights heat

[1807.00459] How To Backdoor Federated Learning - arXiv.org

WebFederated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive local datasets with others. WebFLAME. Unofficial implementation for paper FLAME: Taming Backdoors in Federated Learning, if there is any problem, please let me know. paper FLAME: Taming … how do you develop high-level requirements