# EdgeGuard-AI: Zero-Trust and Load-Aware Federated Scheduling for Secure and Low-Latency IoT Edge Networks

## TL;DR

EdgeGuard-AI is a new framework that combines security and performance in IoT edge networks by making scheduling decisions based on trust and workload.

## Contribution

The novel contribution is a unified trust-driven and load-aware scheduling framework inspired by zero-trust principles.

## Key findings

- EdgeGuard-AI achieves a 97.3% task success rate on a realistic IoT edge security dataset.
- It maintains an average scheduling latency of 58.1 ms during stress periods.
- The framework ensures unsafe offloading remains below 2% and achieves a trust discrimination AUC of 0.971.

## Abstract

Edge computing is now widely used to support real-time and safety-critical IoT services. However, current edge schedulers usually optimize only performance, while security verification and trust assessment are handled as separate modules. This separation creates a practical risk: tasks may be assigned to lightly loaded but compromised edge nodes, or secure nodes may become overloaded, violating latency requirements. We propose EdgeGuard-AI, a unified trust-driven and load-aware scheduling framework inspired by zero-trust security principles for next-generation IoT edge networks. The framework jointly learns dynamic node trust and short-term workload patterns from distributed edge data and integrates both signals into scheduling decisions. Experimental results on a realistic IoT edge security dataset show a task success rate of 97.3 percent, average scheduling latency of 58.1 ms during stress periods, unsafe offloading below 2 percent, and trust discrimination AUC of 0.971.