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.
]]>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 (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…
]]>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…
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