Federated learning enables training a global machine learning model from data distributed
across multiple sites, without having to move the data. This is particularly relevant in …

Federated Machine Learning (FL) can be used effectively in distributed datasets, where data
owners hesitate to share their raw data, as a reliable approach to train an ML algorithm …

In differentially private stochastic gradient descent (DPSGD), gradient clipping and random
noise addition disproportionately affect underrepresented and complex classes and …

Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

Although federated learning can provide privacy protection for individual raw data, some
studies have shown that the shared parameters or gradients under federated learning may …

Wireless sensor networks have been widely used to achieve fine-grained information
collection. However, numerous data acquisition and processing of sensors bring some …

Federated Learning (FL) enables a large number of users to jointly learn a shared machine
learning (ML) model, coordinated by a centralized server, where the data is distributed …

We consider training models with differential privacy (DP) using mini-batch gradients. The
existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD) …

Edge computing and distributed machine learning have advanced to a level that can
revolutionize a particular organization. Distributed devices such as the Internet of Things …

Conventional federated learning directly averages model weights, which is only possible for
collaboration between models with homogeneous architectures. Sharing prediction instead …