The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …

Abstract Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as pipelines in an …

Learning with noisy labels is a common challenge in supervised learning. Existing
approaches often require practitioners to specify noise rates, ie, a set of parameters …

Expectations are a critical factor in determining actions in a great variety of economic
interactions. Hence, being able to measure beliefs is important in many economic …

This survey provides a comprehensive overview of the landscape of crowdsourcing
research, targeted at the machine learning community. We begin with an overview of the …

The problem of peer prediction is to elicit information from agents in settings without any
objective ground truth against which to score reports. Peer prediction mechanisms seek to …

Federated learning (FL) is a promising decentralized deep learning technique that allows
users to collaboratively update models without sharing their own data. However, due to its …

The popularity and applicability of mobile crowdsensing applications are continuously
increasing due to the widespread of mobile devices and their sensing and processing …

The recent proliferation of human-carried mobile devices has given rise to mobile crowd
sensing (MCS) systems that outsource sensory data collection to the public crowd. In order …

Mobile crowd sensing is a new paradigm that enables smart mobile devices to collect and
share various types of sensing data in urban environments. However, new challenges arise …