IEEE P3427

New IEEE Standard - Active - Draft.
This standard establishes a comprehensive framework for federated machine learning (FML) systems of semantic information agents (SIAs), addressing critical needs for efficiency, security, and human comprehension in SIAs environments. By systematically addressing agent formation through FML, including user roles for SIAs and reward mechanisms, the semantic knowledge base is built and data transmission efficiency of SIAs is enhanced. The proposed architecture comprises two primary layers: an information protocol layer that provides mechanisms to support secure and efficient data compression and transmission protocols for SIA interactions; and a semantic layer utilizing a Domain Specific Language (DSL) to maintain information readability among agents.

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 on efficiency and security. • Semantic Layer: This layer focuses on the meaning and interpretation of information through domain-specific languages (DSLs) that are human-understandable. The standard also provides rule-based guidance and optimization strategies for different nodes within federated learning networks, addressing role definitions for agents, reward mechanisms, and multi-agent system regulations.
The purpose of the standard is to facilitate the development, deployment, and governance of federated machine learning systems that incorporate semantic information agents. It aims to enhance interoperability, efficiency, security, and understanding in these complex, multi-agent systems.