Federated learning (FL) has emerged as a promising approach for training machine learning models across distributed data sources while preserving data privacy. However, FL faces significant challenges related to communication overhead and local resource constraints when balancing model requirements and communication capabilities. Particularly in the current era of large language models��
]]>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 is revolutionizing the development of autonomous vehicles (AVs), particularly in cross-country scenarios where diverse data sources and conditions are crucial. Unlike traditional machine learning methods that require centralized data storage, federated learning enables AVs to collaboratively train algorithms using locally collected data while keeping the data decentralized.
]]>XGBoost is a highly effective and scalable machine learning algorithm widely employed for regression, classification, and ranking tasks. Building on the principles of gradient boosting, it combines the predictions of multiple weak learners, typically decision trees, to produce a robust overall model. XGBoost excels with large datasets and complex data structures, thanks to its efficient��
]]>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��
]]>More than 40 million people had their health data leaked in 2021, and the trend is not optimistic. The key goal of federated learning and analytics is to perform data analytics and machine learning without accessing the raw data of the remote sites. That��s the data you don��t own and are not supposed to access directly. But how can you make this happen with a higher degree of confidence?
]]>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 makes it possible for AI algorithms to gain experience from a vast range of data located at different sites.
]]>NVIDIA FLARE 2.2 includes a host of new features that reduce development time and accelerate deployment for federated learning, helping organizations cut costs for building robust AI. Get the details about what��s new in this release. An open-source platform and software development kit (SDK) for Federated Learning (FL), NVIDIA FLARE continues to evolve to enable its end users to leverage��
]]>Unlocking the full potential of artificial intelligence (AI) in financial services is often hindered by the inability to ensure data privacy during machine learning (ML). For instance, traditional ML methods assume all data can be moved to a central repository. This is an unrealistic assumption when dealing with data sovereignty and security considerations or sensitive data like personally��
]]>NVIDIA FLARE (NVIDIA Federated Learning Application Runtime Environment) is an open-source Python SDK for collaborative computation. FLARE is designed with a componentized architecture that allows researchers and data scientists to adapt machine learning, deep learning, or general compute workflows to a federated paradigm to enable secure, privacy-preserving multi-party collaboration.
]]>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��
]]>NVIDIA recently released Clara Train 3.1 for healthcare developers to collaborate on secure, enterprise-grade AI models. Building robust AI can be a challenge for healthcare organizations due to the massive amounts of data required to produce reliable algorithms. With Clara Train, organizations can share and combine their local knowledge to create global models without compromising privacy.
]]>NVIDIA researchers, in collaboration with Owkin scientists, a premier member of NVIDIA Inception, as well as other scientists, this week published a new research paper on Nature Partner Journals Digital Medicine about the future of digital health with federated learning. ��Existing medical data is not fully exploited by machine learning [ML] primarily because it sits in data silos and privacy��
]]>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��
]]>At RSNA 2019, the annual meeting of the Radiological Society of North America, NVIDIA announced updates to the Clara Application Framework that takes healthcare AI to the edge. The Clara Application Framework includes SDKs to build, adapt and deploy AI powered workflows on NVIDIA EGX, its edge AI computing platform. The latest addition to the framework is the Clara AGX SDK��
]]>To help advance medical research while preserving data privacy and improving patient outcomes for brain tumor identification, NVIDIA researchers in collaboration with King��s College London researchers today announced the introduction of the first privacy-preserving federated learning system for medical image analysis. NVIDIA is working with King��s College London and French startup Owkin to enable��
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