Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …

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 …

We propose a novel approach for achieving differential privacy for neural network training
models through compression and memorization of gradients. The compression technique …

With the growing number of Location-Based Social Networks, privacy-preserving point-of-
interest (POI) recommendation has become a critical challenge when helping users discover …

We consider the federated learning problem where data on workers are not independent
and identically distributed (iid). During the learning process, an unknown number of …

Generation of massive data in the digital age leads to possible violations of individual
privacy. The search for personal data becomes an increasingly recurrent exposure today …

Vertical federate learning (VFL) has recently emerged as an appealing distributed paradigm
empowering multi-party collaboration for training high-quality models over vertically …

Vertical federated learning (VFL) is a cloud-edge collaboration paradigm that enables edge
nodes, comprising resource-constrained Internet of Things (IoT) devices, to cooperatively …

The application effect of artificial intelligence (AI) in the field of medical imaging is
remarkable. Robust AI model training requires large datasets, but data collection faces …

Federated Learning (FL) is a well discussed distributed training framework, which allows
scattered clients to collaboratively train a central model without directly sharing raw data …