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 — for example, what users type on their keyboards and the websites they visit — can compromise user privacy. We develop system architectures that enable learning at scale by leveraging advances in machine learning (ML), such as private federated learning (PFL), combined with…
At Apple, we believe privacy is a fundamental human right. Our work to protect user privacy is informed by a set of privacy principles, and one of those principles is to prioritize using on-device processing. By performing computations locally on a user’s device, we help minimize the amount of data…
*Equal Contributors While federated learning (FL) has recently emerged as a promising approach to train machine learning models, it is limited to only preliminary explorations in the domain of automatic speech recognition (ASR). Moreover, FL does not inherently guarantee user privacy and requires the use of differential privacy (DP) for…
Apple researchers are advancing machine learning (ML) and AI through fundamental research that improves the world’s understanding of this technology and helps to redefine what is possible with it. To support the broader research community and help accelerate progress in this field, we share much of our research through publications,…
Model weights: https://huggingface.co/Comfy-Org/Lens PR: https://github.com/Comfy-Org/ComfyUI/pull/14077 You'll need to git the merge pull request if you're…