Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows
multiple clients to jointly train a model without sharing their private data. Recently, many …

Federated learning (FL) has become de facto framework for collaborative learning among
edge devices with privacy concern. The core of the FL strategy is the use of stochastic …

Multilingual natural language understanding, which aims to comprehend multilingual
documents, is an important task. Existing efforts have been focusing on the analysis of …

Distracted driving behavior is known as a leading factor in road traffic injuries and deaths.
Fortunately, rapidly developing deep learning technology has shown its potential in …

Federated learning (FL) is a privacy-preserving machine learning paradigm in which the
server periodically aggregates local model parameters from cli ents without assembling their …

Non-independent and identical distribution (Non-IID) data and model heterogeneity pose a
great challenge for federated learning in cloud-based and edge-based systems. They are …