NVIDIA FLARE: Federated Learning from Simulation to Real-World

Vertical Federated Learning: Concepts, Advances, and Challenges

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy. We provide an exhaustive categorization for VFL settings and privacy-preserving protocols and comprehensively analyze the privacy attacks and defense strategies for each protocol. In the end, we propose a unified framework, termed VFLow, which considers the VFL problem under communication, computation, privacy, as well as effectiveness and fairness constraints. Finally, we review the most recent advances in industrial applications, highlighting open challenges and future directions for VFL.

NVIDIA Rubin CPX Accelerates Inference Performance and Efficiency for 1M+ Token Context Workloads | NVIDIA Technical Blog

Inference has emerged as the new frontier of complexity in AI. Modern models are evolving into agentic systems capable of multi-step reasoning, persistent memory, and long-horizon context—enabling…

Portable Computer: Local-First AI

Run Perplexity Computer locally on NVIDIA DGX Spark. Private, on-device work with cloud escalation when needed.

Mesh LLM: distributed AI computing on iroh

How Mesh LLM pools existing GPU resources across machines into a single OpenAI-compatible API, built on iroh.

Open Source AI Inference Benchmark | InferenceX

Compare AI inference performance across GPUs and frameworks. Real benchmarks on NVIDIA GB200, B200, AMD MI355X, and more. Free, open-source, continuously updated.

RightNow AI - YC-Backed GPU Research Lab

YC-backed GPU research lab building the RightNow CUDA editor, RunInfra inference infra, Forge kernels, and publishing AutoMegaKernel and related papers on arXiv.

Introducing Portable Computer

Perplexity's Portable Computer brings local-first AI to NVIDIA's DGX Spark.

NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference

Thanks to NVIDIA’s early access program, we are thrilled to get our hands on the NVIDIA DGX™ Spark. It’s quite an unconventional system, as NVIDIA rarely releases compact, all-in-one machines that bri...

Open-Source Agentic Inference Benchmark | InferenceX

Compare AgentX, InferenceX's long-context, multi-turn coding scenario, with fixed-sequence AI inference across chips and frameworks. Public NVIDIA and AMD runs update when configurations change.

NVIDIA and Microsoft Reinvent Windows PCs for the Age of Personal AI

RTX Spark — a 1-Petaflop Superchip, the Full CUDA and RTX Ecosystem, and Windows-Native Agents — a New Beginning for Personal Computers News Summary: NVIDIA RTX Spark powers the world’s first Windows PCs purpose-built for personal agents, featuring 1 petaflop of AI performance, industry-leading power efficiency, full-stack NVIDIA AI and graphics technology, and up to 128GB of unified memory. NVIDIA and Microsoft collaborate to deliver a native Windows experience for personal agents, including new security primitives and NVIDIA OpenShell to run agents securely on primary devices. RTX Spark lets creators, AI developers and gamers render ultralarge 90GB+ 3D scenes, edit 12K 4:2:2 video, generate 4K AI videos, run 120B-parameter LLMs with up to 1 million tokens context using agents locally, and play AAA games at 1440p and over 100 frames per second. Adobe is rearchitecting Photoshop and Premiere from the ground up for RTX Spark to deliver 2x faster AI and graphics performance. RTX

nvidia/parakeet-tdt-0.6b-v2 · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

NVIDIA Inception Program for Startups

Join NVIDIA Inception’s global network of data science, HPC, and gen AI startups.

NVIDIA Inception Program for Startups

Join NVIDIA Inception’s global network of data science, HPC, and gen AI startups.

Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security | NVIDIA Technical Blog

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing…

Robust Federated Inference

Federated inference, in the form of one-shot federated learning, edge ensembles, or federated ensembles, has emerged as an attractive solution to combine predictions from multiple models. This paradigm enables each model to remain local and proprietary while a central server queries them and aggregates predictions. Yet, the robustness of federated inference has been largely neglected, leaving them vulnerable to even simple attacks. To address this critical gap, we formalize the problem of robust federated inference and provide the first robustness analysis of this class of methods. Our analysis of averaging-based aggregators shows that the error of the aggregator is small either when the dissimilarity between honest responses is small or the margin between the two most probable classes is large. Moving beyond linear averaging, we show that problem of robust federated inference with non-linear aggregators can be cast as an adversarial machine learning problem. We then introduce an advanced technique using the DeepSet aggregation model, proposing a novel composition of adversarial training and test-time robust aggregation to robustify non-linear aggregators. Our composition yields significant improvements, surpassing existing robust aggregation methods by 4.7 - 22.2% in accuracy points across diverse benchmarks.