In this work, besides improving prediction accuracy, we study whether personalization could
bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks …

The decentralized nature of federated learning makes detecting and defending against
adversarial attacks a challenging task. This paper focuses on backdoor attacks in the …

The decentralized nature of federated learning makes detecting and defending against
adversarial attacks a challenging task. This paper focuses on backdoor attacks in the …

Federated Learning (FL) as a distributed learning paradigm that aggregates information
from diverse clients to train a shared global model, has demonstrated great success …

Federated Learning (FL) is a distributed machine learning paradigm that allows multiple
clients to train a global model collaboratively without sharing their local training data. Due to …

Malicious clients can attack federated learning systems using malicious data, including
backdoor samples, during the training phase. The compromised global model will perform …

Backdoors on federated learning will be diluted by subsequent benign updates. This is
reflected in the significant reduction of attack success rate as iterations increase, ultimately …

Backdoor attacks aim to manipulate a subset of training data by injecting adversarial triggers
such that machine learning models trained on the tampered dataset will make arbitrarily …

Federated Learning (FL) is a new machine learning framework, which enables millions of
participants to collaboratively train machine learning model without compromising data …

Federated learning (FL) enables the training of models among distributed clients without
compromising the privacy of training datasets, while the invisibility of clients datasets and the …