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

Lane detection in driving scenes is an important module for autonomous vehicles and
advanced driver assistance systems. In recent years, many sophisticated lane detection …

Federated learning can combine a large number of scattered user groups and train models
collaboratively without uploading data sets, so as to avoid the server collecting user …

With powerful parallel computing GPUs and massive user data, neural-network-based deep
learning can well exert its strong power in problem modeling and solving, and has archived …

Federated learning (FL) strives to enable collaborative training of machine learning models
without centrally collecting clients' private data. Different from centralized training, the local …

How to train a machine learning model while keeping the data private and secure? We
present CodedPrivateML, a fast and scalable approach to this critical problem …

Big data collection means collecting large volumes of data to have insight into better
business decisions and greater customer satisfaction. Securing big data is difficult not just …

Nowadays, the booming demand of big data analytics and the constraints of computational
ability and network bandwidth have made it difficult for a stand-alone agent/service provider …

Auction is an effective way to allocate goods or services to bidders who value them the most.
The rapid growth of e-auctions facilitates online transactions but poses new and distinctive …

Privacy leakage is a serious issue in spatial crowdsourcing in various scenarios. In this
paper, we study privacy protection in spatial crowdsourcing. The main challenge is to …