Categories: FAANG

FLAIR: Federated Learning Annotated Image Repository

Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification…

pfl-research: Simulation Framework for Accelerating Research in Private Federated Learning

Federated Learning (FL) is an emerging ML training paradigm where clients own their data and collaborate to train a global model without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not…

May 15, 2024

In "FAANG"

Medical Centers Tap AI, Federated Learning for Better Cancer Detection

A committee of experts from top U.S. medical centers and research institutes is harnessing NVIDIA-powered federated learning to evaluate the impact of federated learning and AI-assisted annotation to train AI models for tumor segmentation. Federated learning is a technique for developing more accurate, generalizable AI models trained on data across…

September 21, 2024

In "FAANG"

Apple Workshop on Privacy-Preserving Machine Learning 2024

At Apple, we believe privacy is a fundamental human right. It’s also one of our core values, influencing both our research and the design of Apple’s products and services. Understanding how people use their devices often helps in improving the user experience. However, accessing the data that provides such insights…

August 30, 2024

In "FAANG"

AI Generated Robotic Content