Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide
attention from society and individuals. It is desirable to make the data available but invisible …

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning
framework, where multiple data owners cooperate to train a global model without any …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

With federated learning, one of the most notable features is that it can update global model
parameter without using the users' local data. However, various security and privacy …

Federated learning has gained extensive interest in recent years owing to its ability to
update model parameters without obtaining raw data from users, which makes it a viable …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

With the rapid development of the Internet of Medical Things (IoMT), medical institutions are
accumulating vast amounts of medical data and aiming to utilize this data to train high …

Federated learning enables multiple participants to cooperatively train a model, where each
participant computes gradients on its data and a coordinator aggregates gradients from …

This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning
(FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The …