CMU researchers propose OpenFLAME: a federated and decentralized location service
Maps are widely used today and are useful in numerous location-based applications, including navigation, ride-sharing, fitness tracking, gaming, robotics, and ...
Maps are widely used today and are useful in numerous location-based applications, including navigation, ride-sharing, fitness tracking, gaming, robotics, and ...
Graph neural networks (GNNs) have emerged as powerful tools for capturing complex interactions in real-world entities and finding applications across ...
Digital twin (DT) technology is becoming increasingly popular as a method of providing Internet of Things (IoT) devices with dynamic ...
*Equal taxpayers While federated learning (FL) has recently emerged as a promising approach to training machine learning models, it is ...
We return to the problem of designing scalable protocols for private statistics and private federated learning when each device has ...
In practice, training using federated learning can be much slower than standard centralized training. This severely limits the amount of ...
Federated learning (FL) is an emerging machine learning training paradigm in which clients own their data and collaborate to train ...
This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, ...
In recent research, a team of Google Research researchers introduced FAX, an advanced software library built on top of JavaScript ...
This post is co-written with Chaoyang He, Al Nevarez and Salman Avestimehr from FedML. Many organizations are implementing machine learning ...