Standards Database

This recommended practice provides a framework for the use of large-scale pre-trained deep learning models, including approaches, taxonomies, related roles, and activities, software toolkits, application ability key performance indicators as well as assessment means. © Copyright 2024 IEEE – All…

This recommended practice specifies principles, approaches, and key performance indicators for distributed training and inference of large-scale deep learning models. © Copyright 2024 IEEE – All rights reserved.

The development and application of federated machine learning are facing the critical challenges about how to balance the tradeoff among privacy, security, performance, and efficiency, how to realize supervision covering the whole life cycle and how to get the explainable…

This guide provides data contribution measurement methods for federated machine learning. The document provides guidance with respect to data trading feasibility for federated machine learning. The guide describes three main aspects: 1) Framework for data contribution evaluation of federated machine…

This guide defines a machine learning framework that allows a computing task to be decomposed and distributed across edge and cloud nodes. This guide provides a blueprint for data usage, model learning, and computing collaboration in edge computing environments while…

New broadcasting technologies driven by artificial intelligence (AI) are being introduced to the broadcasting workflow. These technologies are intended to increase productivity, efficiency and creative opportunities during programme production, and to convey information to viewers quickly, accurately and automatically. This…

This standard establishes a comprehensive framework for mitigating security risks, privacy leaking in the development, deployment, and use of generative pretrained AI models. The framework encompasses requirements concerning security risks, functional and non-functional requirements, and evaluation metrics such as transparency,…

This standard provides definitions, terms, frameworks, and general requirements for systems that apply pre-trained large language models (LLM) in the smart home industry. It covers foundational platforms, model capabilities, scenario applications, and safety requirements. © Copyright 2024 IEEE – All…

This standard specifies a domain-specific architecture for Large Language Models (LLMs) Management Platforms that includes: functional, service, and deployment requirements. © Copyright 2024 IEEE – All rights reserved.

This recommended practice follows a safety by design approach by providing recommendations for developing, deploying, and maintaining generative artificial intelligence models with adequate safeguards against child sexual abuse. The document provides a framework that expands child safety to the entire…

The recommended practice outlines a process model that leverages artificial intelligence imaging and data processing technologies for improved emissions information reporting. It provides textile producers with comprehensive data on fabrics, including their origins and metadata. This approach enables stakeholders to…

This recommended practice provides a holistic framework for AI foundation models. The framework allows sharing of the common computing resources, data and infrastructure in the usage(s) and application(s) of AI Foundation models. The detailed technical requirements for the holistic framework…

This standard provides: 1. Agent Components: The building blocks and architectural elements that constitute an educational Large Language Model (LLM) agent. 2. Agent Interoperability Protocols: A communication protocol for interaction between different LLM agents and other components in the educational…

This standard defines a comprehensive framework for federated machine learning of semantic information agents. It targets two primary layers: • Information Protocol Layer: This layer focuses on binary compression and protocol-level data structures for federated information exchange, with an emphasis…

This standard addresses the evaluation of safety, explainability, and stability of algorithms implementing autonomous driving levels, as defined by SAE J3016. The standard specifies the minimum requirements for experimental setups, data flow charts, algorithm pipelines, and automated quantitative evaluation metrics…