To defend the inference attacks and mitigate the sensitive information leakages in Federated
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …

Federated learning is a promising learning paradigm that allows collaborative training of
models across multiple data owners without sharing their raw datasets. To enhance privacy …

Background The collection, storage, and analysis of large data sets are relevant in many
sectors. Especially in the medical field, the processing of patient data promises great …

The proposal of federated learning solves problems of data silos and privacy protection in
the field of artificial intelligence. However, privacy attacks can infer or reconstruct sensitive …

Federated Learning (FL) has been recently receiving increasing consideration from the
cybersecurity community as a way to collaboratively train deep learning models with …

Federated learning (FL) is designed to preserve data privacy during model training, where
the data remains on the client side (ie, IoT devices), and only model updates of clients are …

Abstract Federated Learning for the Internet of Things (FL for IoT) contributes to the
enhancement of security in smart cities. However, the privacy disclosure attacks within the …

Federated learning (FL) aims to collaboratively train the global model in a distributed
manner by sharing the model parameters from local clients to a central server, thereby …

Consumer electronic devices often involve processing and analyzing a large amount of user
personal data. Nevertheless, owing to apprehensions regarding privacy and security, users …

Federated learning (FL) is a distributed machine learning method that effectively protects
personal data. Many studies on federated learning assumed that all clients have consistent …