XGBoost is a machine learning algorithm widely used for tabular data modeling. To expand the XGBoost model from single-site learning to multisite collaborative training, NVIDIA has developed Federated XGBoost, an XGBoost plugin for federation learning. It covers vertical collaboration settings to jointly train XGBoost models across decentralized data sources, as well as horizontal histogram-based��
]]>
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.
]]>
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��
]]>
In the era of big data and distributed computing, traditional approaches to machine learning (ML) face a significant challenge: how to train models collaboratively when data is decentralized across multiple devices or silos. This is where federated learning comes into play, offering a promising solution that decouples model training from direct access to raw training data. One of the key��
]]>
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��
]]>
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.
]]>
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��
]]>