# Federated Learning A decentralized machine learning approach that keeps training data on devices and builds a shared model from locally computed updates. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE DevFeed: [Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Federated Learning](), [Docker](), [Kubernetes](), [Nvidia](), [Server](), [dataset](), [Deployment]() Tags: [architecture](), [compute](), [configuration](), [container](), [data-analytics-processing](), [data-science](), [dataset](), [deployment](), [docker](), [docker-container](), [federated-learning](), [gpu](), [job](), [kubernetes](), [nvidia](), [nvidia-flare](), [server]() ### AI overview This article explains how NVIDIA FLARE scales federated learning across sites with different infrastructure, including Docker, Kubernetes, and Slurm. Its two-layer architecture separates persistent federation services from on-demand job execution, while allowing each site to retain local control over compute, data, secrets, and scheduling. ### Source excerpt Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the... ## Social learning: Collaborative learning with large language models DevFeed: [Social learning: Collaborative learning with large language models]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-07T18:15:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Large Language Model](), [Federated Learning](), [datasets](), [Learning]() Tags: [datasets](), [federated-learning](), [few-shot](), [few-shot-learning](), [large-language-models](), [machine-intelligence](), [natural-language-processing](), [privacy](), [research](), [use-cases]() ### AI overview This Google Research article presents a privacy-aware social learning framework in which large language models teach one another using natural language rather than shared gradients. It describes evaluations involving spam detection, grade-school mathematics, and text-based question answering, including settings where labeled examples cannot be directly shared because of privacy concerns. ### Source excerpt Posted by Amirkeivan Mohtashami, Research Intern, and Florian Hartmann, Software Engineer, Google Research Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be beneficial for them to learn effectively from each other, much like people do in social settings, which would allow LLM-based agents to improve each other's performance. To discuss the learning processes of humans, Bandura and Walters described the concept of social learning in 1977, outlining different models of observational learning used by people. One common method of learning from others is through a verbal instruction (e.g., from a teacher) that describes how to engage in a particular behavior. Alternatively, learning can happen through a live model by mimicking a live example of the behavior. Given the success of LLMs mimicking human communication, in our paper "Social Learning: Towards Collaborative Learning with Large Language Models", we investigate whether LLMs are able to learn from each other using social learning. To this end, we outline a framework for social learning in which LLMs share knowledge with each other in a privacy-aware manner using natural language. We evaluate the effectiveness of our framework on various datasets, and propose quantitative methods that measure privacy in this setting. In contrast to previous approaches to collaborative learning, such as common federated learning approaches that often rely on gradients, in our framework, agents teach each other purely using natural language. Social learning for LLMs To extend social learning to language models, we consider the scenario where a student LLM should learn to solve a task from multiple teacher entities that already know that task. In our paper, we evaluate the student's performance on a variety of tasks, such as spam detection i ## Advances in private training for production on-device language models DevFeed: [Advances in private training for production on-device language models]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-02-21T20:15:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Language models](), [Federated Learning](), [Google](), [Mobile]() Tags: [differential-privacy](), [federated-learning](), [gboard](), [google](), [language-models](), [on-device](), [on-device-learning](), [phones](), [privacy](), [production](), [research](), [responsible-ai](), [training]() ### AI overview This Google Research article describes advances in privately training Gboard's on-device language models. It explains how federated learning keeps training data on users' devices while differential privacy provides formal privacy guarantees. The article reports that all Gboard next-word-prediction neural language models use federated learning with formal differential privacy guarantees, across more than 30 models, 7 or more languages, and 15 or more countries. ### Source excerpt Posted by Zheng Xu, Research Scientist, and Yanxiang Zhang, Software Engineer, Google Language models (LMs) trained to predict the next word given input text are the key technology for many applications [1, 2]. In Gboard, LMs are used to improve users' typing experience by supporting features like next word prediction (NWP), Smart Compose, smart completion and suggestion, slide to type, and proofread. Deploying models on users' devices rather than enterprise servers has advantages like lower latency and better privacy for model usage. While training on-device models directly from user data effectively improves the utility performance for applications such as NWP and smart text selection, protecting the privacy of user data for model training is important. Gboard features powered by on-device language models. In this blog we discuss how years of research advances now power the private training of Gboard LMs, since the proof-of-concept development of federated learning (FL) in 2017 and formal differential privacy (DP) guarantees in 2022. FL enables mobile phones to collaboratively learn a model while keeping all the training data on device, and DP provides a quantifiable measure of data anonymization. Formally, DP is often characterized by (ε, δ) with smaller values representing stronger guarantees. Machine learning (ML) models are considered to have reasonable DP guarantees for ε=10 and strong DP guarantees for ε=1 when δ is small. As of today, all NWP neural network LMs in Gboard are trained with FL with formal DP guarantees, and all future launches of Gboard LMs trained on user data require DP. These 30+ Gboard on-device LMs are launched in 7+ languages and 15+ countries, and satisfy (ɛ, δ)-DP guarantees of small δ of 10-10 and ɛ between 0.994 and 13.69. To the best of our knowledge, this is the largest known deployment of user-level DP in production at Google or anywhere, and the first time a strong DP guarantee of ɛ < 1 is announced for models trained directly on