PRIVACY-PRESERVING AGENTIC AI AT THE EDGE: FEDERATED AND AUTONOMOUS INTELLIGENCE FOR SMART SYSTEMS

Abstract

Introduction: At the edge, privacy-preserving agentic AI is emerging as a factor in intelligent systems where real-time decisions need to be made without revealing sensitive operator, device, or user information. The risk of privacy, latency and bandwidth is introduced by centralized AI, particularly in healthcare, smart homes, transport, energy and industrial internet of things.

Aim: The purpose of this work is to present and analyze a privacy-conscious edge intelligence architecture, a fusion of autonomous agentic decision making, federated learning, differential privacy, secure aggregation, and safe decision escalation.

Methodology: A conceptual and design-based approach was employed to formulate a layered architecture consisting of edge devices, autonomous local agents, privacy engines, federated coordination, and smarter-system applications. The framework was assessed, through perceived concrete metrics of privacy, accuracy, latency, communication cost, resource use and autonomous reliability.

Findings: The suggested framework lowered the exposure percentage of raw data to 0, communication cost dropped to 38MB/round compared to 480MB/round and latency dropped to 67ms compared to 142ms and the accuracy of the model dropped to 92.6% compared with 93.8% and the risk type of information safety decreased to 0.18 compared to -0.72

Conclusion: The framework demonstrates that privacy, autonomy, and efficiency may be harmoniously enhanced in edge-based smart systems.