SECURITY-AWARE OPTIMIZATION OF INTELLIGENT IOT NETWORKS IN SMART AGRICULTURE USING FEDERATED AI APPROACH: TRADE-OFFS BETWEEN PERFORMANCE, RELIABILITY, AND TRUST

Abstract

This research introduces a novel security-aware optimization framework for intelligent Internet of Things (IoT) networks in smart agriculture, leveraging Federated Artificial Intelligence (AI) to address the critical trade-offs between performance, reliability, and trust. We formulate a multi-objective optimization problem that simultaneously maximizes network performance metrics while ensuring robust security guarantees and maintaining computational efficiency. The proposed Federated Security-Aware Optimization (FSAO) framework employs differential privacy, secure multi-party computation, and blockchain-based trust management to protect sensitive agricultural data while enabling collaborative learning across distributed agricultural sites. Our mathematical formulation incorporates threat modeling using game-theoretic approaches and implements Byzantine-resilient aggregation for federated learning. Extensive experiments across three agricultural testbeds with 1,500+ IoT devices demonstrate that FSAO achieves 89.7% attack detection accuracy with only 12.3% performance overhead, while maintaining 94.5% model accuracy under coordinated poisoning attacks. Statistical analysis confirms significant improvements in security metrics (p < 0.001) while preserving network performance within acceptable bounds. The framework establishes optimal operating points across the performance-reliability-trust trade-off space, providing practical guidelines for secure agricultural IoT deployments in real-world scenarios.