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Found 8 papers, 1 papers with code
Phoenix: A Federated Generative Diffusion Model
Our proposed method Phoenix is an unconditional diffusion model that leverages strategies to improve the data diversity of generated samples even when trained on data with statistical heterogeneity or Non-IID (Non-Independent and Identically Distributed) data.
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local.
Analysing Fairness of Privacy-Utility Mobility Models
Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories.
Personalized Emotion Detection using IoT and Machine Learning
The Medical Internet of Things, a recent technological advancement in medicine, is incredibly helpful in providing real-time monitoring of health metrics.
Privacy-Aware Adversarial Network in Human Mobility Prediction
As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing.
Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities
Mobile Crowdsensing has become main stream paradigm for researchers to collect behavioral data from citizens in large scales.
Privacy-Aware Human Mobility Prediction via Adversarial Networks
Our proposed architecture reports a Pareto Frontier analysis that enables the user to assess this trade-off as a function of Lagrangian loss weight parameters.
Fairness in Federated Learning for Spatial-Temporal Applications
Federated learning involves training statistical models over remote devices such as mobile phones while keeping data localized.
