For data isolated islands and privacy issues, federated learning has been extensively
invoking much interest since it allows clients to collaborate on training a global model using …

The increasing awareness of privacy and the adoption of data regulations challenge the
traditional trajectory publication framework in which a trusted server has access to the raw …

Local differential privacy (LDP), where each user obfuscates their data locally before
sending it to an untrustworthy data collector, provides a strict privacy guarantee for users' …

Traditional privacy-preserving technologies have been unable to provide adequate
protection and are vulnerable to background knowledge. MapReduce is parallel distributed …

The existing work has conducted in-depth research and analysis on global differential
privacy (GDP) and local differential privacy (LDP) based on information theory. However, the …

As a distributed machine learning framework, federated learning enables a multitude of
participants to train a joint model privately by keeping training data locally. The federated …

Numerous emergencies have happened recently on a worldwide scale, necessitating and
occasionally requiring relevant decision-makers to focus excessively on the decision …

By integrating the traditional power grid with information and communication technology,
smart grid achieves dependable, efficient, and flexible grid data processing. The smart …

Federated Learning is an emerging privacy-preserving machine learning framework, mainly
based on the federation of multiple devices that a central server coordinates. The central …

With the development of IoT technology, smart grid has gradually replaced the traditional
grid. Smart grid is convenient and fast. It can provide real-time residential electricity …