Federated learning came into being with the increasing concern of privacy security, as
people's sensitive information is being exposed under the era of big data. It is an algorithm …

Security at the physical layer (PHY) is a salient research topic in wireless systems, and
machine learning (ML) is emerging as a powerful tool for providing new data-driven security …

Existing learning rate schedules that do not require specification of the optimization stop**
step $ T $ are greatly out-performed by learning rate schedules that depend on $ T $. We …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Recent studies show that private training data can be leaked through the gradients sharing
mechanism deployed in distributed machine learning systems, such as federated learning …

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …

Performing highly accurate representation learning on a high-dimensional and sparse
(HiDS) matrix is of great significance in a big data-related application such as a …

Abstract We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose
approach for uncertainty representation and calibration in deep learning. Stochastic Weight …

Next Point-of-Interest (POI) recommendation which is of great value to both users and POI
holders is a challenging task since complex sequential patterns and rich contexts are …