NVIDIA and the PyTorch team at Meta announced a groundbreaking collaboration that brings federated learning (FL) capabilities to mobile devices through the integration of NVIDIA FLARE and ExecuTorch. NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that enables researchers and data scientists to adapt existing machine learning or deep learning workflows to a federated paradigm.
]]>In recent years, open-source systems like Flower and NVIDIA FLARE have emerged as pivotal tools in the federated learning (FL) landscape, each with its unique focus. Flower champions a unified approach to FL, enabling researchers and developers to design, analyze, and evaluate FL applications with ease. Over time, it has amassed a rich suite of strategies and algorithms…
]]>Federated learning (FL) is experiencing accelerated adoption due to its decentralized, privacy-preserving nature. In sectors such as healthcare and financial services, FL, as a privacy-enhanced technology, has become a critical component of the technical stack. In this post, we discuss FL and its advantages, delving into why federated learning is gaining traction. We also introduce three key…
]]>In the ever-evolving landscape of large language models (LLMs), effective data management is a key challenge. Data is at the heart of model performance. While most advanced machine learning algorithms are data-centric, necessary data can’t always be centralized. This is due to various factors such as privacy, regulation, geopolitics, copyright issues, and the sheer effort required to move vast…
]]>Large language models (LLMs), such as GPT, have emerged as revolutionary tools in natural language processing (NLP) due to their ability to understand and generate human-like text. These models are trained on vast amounts of diverse data, enabling them to learn patterns, language structures, and contextual relationships. They serve as foundational models that can be customized to a wide range of…
]]>One of the main challenges for businesses leveraging AI in their workflows is managing the infrastructure needed to support large-scale training and deployment of machine learning (ML) models. The NVIDIA FLARE platform provides a solution: a powerful, scalable infrastructure for federated learning that makes it easier to manage complex AI workflows across enterprises. NVIDIA FLARE 2.3.0…
]]>Federated learning (FL) has become a reality for many real-world applications. It enables multinational collaborations on a global scale to build more robust and generalizable machine learning and AI models. For more information, see Federated learning for predicting clinical outcomes in patients with COVID-19. NVIDIA FLARE v2.0 is an open-source FL SDK that is making it easier for data…
]]>In NVIDIA Clara Train 4.0, we added homomorphic encryption (HE) tools for federated learning (FL). HE enables you to compute data while the data is still encrypted. In Clara Train 3.1, all clients used certified SSL channels to communicate their local model updates with the server. The SSL certificates are needed to establish trusted communication channels and are provided through a third…
]]>Deep neural networks (DNNs) have been successfully applied to volume segmentation and other medical imaging tasks. They are capable of achieving state-of-the-art accuracy and can augment the medical imaging workflow with AI-powered insights. However, training robust AI models for medical imaging analysis is time-consuming and tedious and requires iterative experimentation with parameter…
]]>AI requires massive amounts of data. This is particularly true for industries such as healthcare. For example, training an automatic tumor diagnostic system often requires a large database in order to capture the full spectrum of possible anatomies and pathological patterns. In order to build robust AI algorithms, hospitals and medical institutions often need to collaboratively share and combine…
]]>Deep Learning in medical imaging has shown great potential for disease detection, localization, and classification within radiology. Deep Learning holds the potential to create solutions that can detect conditions that might have been overlooked and can improve the efficiency and effectiveness of the radiology team. However, for this to happen data scientists and radiologists need to collaborate…
]]>The NVIDIA Transfer Learning Toolkit is now NVIDIA TAO Toolkit. The growing volume of clinical data in medical imaging slows down identification and analysis of specific features in an image. This reduces the annotation speed at which radiologists and imaging technicians capture, screen, and diagnose patient data. The demand for artificial intelligence in medical image analysis has…
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