Sparse Federated Representation Learning for precision oncology clinical workflows with embodied agent feedback loops The Epiphany That Changed My Research Direction It was 2:47 AM on a Tuesday in February 2024 when I had what I can only describe as a research epiphany. I was hunched over my workstation, staring at yet another failed federated learning convergence curve—the loss function oscillating wildly like a seismograph during an earthquake. My PhD student had been trying to train a pan-cancer mutation classifier across 17 hospital sites, each with their own private genomic datasets, and the results were... underwhelming. I'd been working in federated learning for medical imaging for years, but oncology genomics was a different beast entirely. The data was sparse—not just in the "missing values" sense, but fundamentally sparse. A single patient's tumor biopsy might yield 20,000 gene expression measurements, yet only 10-50 genes would be differentially expressed.…