WebAbstract. As a paradigm of distributed machine learning, federated learning is widely used in various real scenarios due to its excellent privacy protection performance on preventing local data from being disclosed. WebDue to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the performance of the trained model on specific sub-tasks (e.g., by classifying green cars as frogs).A range of FL backdoor attacks have been introduced in the literature, but also …
Attack of the Tails: Yes, You Really Can Backdoor …
WebCan You Really Backdoor Federated Learning? Abstract: The decentralized nature of federated learning makes detecting and defending against adversarial attacks a … WebDue to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the … firefox html5 播放器
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning ...
WebDec 5, 2024 · Can you really backdoor federated learning?arXiv preprint arXiv:1911.07963(2024). Google Scholar; Rashish Tandon, Qi Lei, Alexandros G Dimakis, and Nikos Karampatziakis. 2024. Gradient coding: Avoiding stragglers in distributed learning. In ICML. Google Scholar; Berkay Turan, Cesar A Uribe, Hoi-To Wai, and … WebJan 1, 2024 · This paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining good ... WebThis paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining a good performance on the main task. Unlike existing works, we allow non-malicious clients to have correctly labeled samples from the targeted tasks. ethel bellamy obituary