Due to the powerful representation ability and superior performance of Deep Neural
Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from …

Federated learning (FL) was once considered secure for keeping clients' raw data locally
without relaying on a central server. However, the transmitted model weights or gradients …

Federated learning (FL) is a distributed, privacy-preserving learning paradigm where a joint
model is trained on private data stored on client devices. Data owners (clients) train models …

Federated learning (FL) is a distributed machine learning paradigm that enables scattered
clients to collaboratively train a shared global model. FL is suitable for privacy-preserving …

In federated learning (FL), malicious clients could manipulate the predictions of the trained
model through backdoor attacks, posing a significant threat to the security of FL systems …

Federated learning (FL) is a promising technical support to the vision of ubiquitous artificial
intelligence in the sixth generation (6G) wireless communication network. However …

Federated learning (FL) enables the full utilization of decentralized training without raw data.
However, various attacks still threaten the training process of FL. To address these …

In this work, we consider the problem of designing secure and efficient federated learning
(FL) frameworks. Existing solutions either involve a trusted aggregator or require …

Federated learning (FL) has emerged as a promising privacy-preserving machine learning
paradigm, enabling data owners to collaboratively train a joint model by sharing model …

Backdoor attacks pose a significant threat to the security of federated learning systems.
However, existing research primarily focuses on backdoor attacks and defenses within the …