3 accepted papers
Federated Learning (FL) is considered a new paradigm of privacy-preserving machine learning since the server trains a machine learning model in a distributed way without collecting clients’ raw data but only local models. However, recent studies show that FL suffers inference attacks. Sensitive info…
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differentially private federated learning has been intensively studied. The existing works are mainly based on the curator model o…
In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a correspondi…