IEEE 2986-2023

IEEE 2986-2023 is the standard for privacy and security in federated machine learning, offering guidance for computing and processing systems that train models across distributed data sources. It helps define how organizations can coordinate learning without centralizing sensitive data, which is important when data sharing is limited by policy, confidentiality, or operational constraints. By focusing on privacy and security considerations, IEEE 2986-2023 supports more controlled and trustworthy machine learning deployments.

Overview of IEEE 2986-2023

This recommended practice addresses the technical context of federated machine learning, where model training is performed across multiple devices, sites, or organizations while data remains local. IEEE 2986-2023 is intended to help identify privacy and security concerns that can arise in distributed learning workflows, including communication, coordination, and model update exchange. For computing and processing applications, it provides a focused reference for aligning design choices with privacy-aware and security-conscious practices.

Typical use cases

This standard is relevant for systems that need collaborative machine learning without moving raw data into a central repository. Common use cases may include edge or distributed analytics, multi-site data science projects, and environments where sensitive records must stay within their source systems. IEEE 2986-2023 can also support teams evaluating federated learning platforms, model update pipelines, and governance controls for secure training across connected computing nodes.

Why it matters

In federated machine learning, privacy and security decisions directly affect data exposure, model integrity, and operational trust. IEEE 2986-2023 matters because it gives organizations a consistent reference for designing, reviewing, and comparing distributed learning approaches. That can improve procurement decisions, internal compliance checks, and technical testing for systems that exchange model parameters or updates rather than raw data. Using IEEE 2986-2023 may also help reduce risk when deploying collaborative AI workflows.

• Privacy and security guidance for federated learning
• Distributed model training across local data sources
• Technical focus for computing and processing systems
• Support for review, testing, and governance checks