Sparse Federated Representation Learning for smart agriculture microgrid orchestration under multi-jurisdictional compliance Introduction: A Personal Learning Journey It was during a late-night debugging session in my home lab, surrounded by Raspberry Pi clusters simulating distributed energy resources, that I stumbled upon a profound realization. I had been wrestling with a seemingly intractable problem: how to orchestrate a smart agriculture microgrid—spanning three different jurisdictions in the Pacific Northwest—while ensuring each region’s unique regulatory compliance was met, all without centralizing sensitive farm data. My initial approach, a vanilla federated learning framework using TensorFlow Federated, was failing spectacularly. The model wasn’t converging; communication overhead was crushing; and, worst of all, the learned representations were so dense and entangled that they violated the privacy guarantees I had naively assumed.…