Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

This survey examines approaches to promote Collaborative Learning in distributed systems
for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of …

In a vertical federated learning (VFL) scenario where features and models are split into
different parties, it has been shown that sample-level gradient information can be exploited …

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its
inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of …

Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine
learning (ML). However, secure training of large-scale ML models currently requires a …

From Cloud Computing to Sky Computing Page 1 From Cloud Computing to Sky Computing Ion
Stoica and Scott Shenker UC Berkeley Abstract We consider the future of cloud computing and …

Federated analytics (FA) is a privacy-preserving framework for computing data analytics
over multiple remote parties (eg, mobile devices) or silo-ed institutional entities (eg …

With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT)
and machine learning technologies have been widely used to achieve the intelligent control …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …