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 …

With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

The increasing size of data generated by smartphones and IoT devices motivated the
development of Federated Learning (FL), a framework for on-device collaborative training of …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

There are two categories of methods in Federated Learning (FL) for joint training across
multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) …

Recent developments in deep learning have led to great success in various natural
language processing (NLP) tasks. However, these applications may involve data that …

Triangle counting in networks under LDP (Local Differential Privacy) is a fundamental task
for analyzing connection patterns or calculating a clustering coefficient while strongly …

Machine learning models can be trained with formal privacy guarantees via differentially
private optimizers such as DP-SGD. In this work, we focus on a threat model where the …

Decentralized learning is appealing as it enables the scalable usage of large amounts of
distributed data and resources (without resorting to any central entity), while promoting …

Nowadays mobile positioning devices, such as global navigation satellite systems (GNSS)
but also external sensor technology like cameras allow an efficient online collection of …