Edge AI for Autonomous Operations in Unreliable Networks: Quantization, Federated Learning, and Energy-Aware Inference at the Extreme Edge
The shift from cloud-centric artificial intelligence to true edge autonomy represents a fundamental shift in how industrial and mission-critical systems are engineered. As of 2026, the reliance on persistent, high-bandwidth connections to centralized hyperscale data centers has proven to be an untenable architecture for operations in "nasty" networks, environments characterized by intermittent links, extreme latency, and harsh physical constraints. Whether in the deep galleries of an autonomous mine, the forward operating bases of a modern defense force, or the volatile offshore environments of wind energy farms, the requirement for localized intelligence has moved from a theoretical advantage to an operational necessity.
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