Edge Deployment

The continuous-monitoring footprint is reported as 617.1 KB. Fed-FSTQ fit within a 2GB edge memory budget at 1450 MB peak memory. Fed-FSTQ produced up to a 1.55 times end-to-end speedup on Jetson-class inference. The MP-IB monitoring configuration is reported to run end-to-end in 23.4 ms on Raspberry Pi Zero 2W. The evaluation is not a full live mobile network deployment because it uses measured Jetson computation and emulated LTE communication. Cortex-M7 deployment was not physically validated. Edge benchmarks were measured on Raspberry Pi Zero 2W with TensorFlow Lite and custom ARMv8-A NEON INT4 kernels. The monitoring deployment uses an INT8 encoder, INT4 state head, and INT8 agitation MLP. The evaluation uses LTE profiles including controlled 20 Mbps throughput and heterogeneous LTE with slow stragglers around 0.5 to 2 Mbps. Other modalities, larger cross-device populations, longer contexts, and production-scale privacy mechanisms remain open evaluation areas.