From distributed machine learning to federated learning: A survey
In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …
users, various regions or organizations. Because of laws or regulations, the distributed data …
PPFL: Privacy-preserving federated learning with trusted execution environments
We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …
A systematic review of federated learning from clients' perspective: challenges and solutions
Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …
processing by allowing clients to train intermediate models on their devices with locally …
Ppfl: Enhancing privacy in federated learning with confidential computing
Mobile networks and devices provide the users with ubiquitous connectivity, while many of
their functionality and business models rely on data analysis and processing. In this context …
their functionality and business models rely on data analysis and processing. In this context …