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The problem of maximizing the weighted average of performance certificates in the presence of uncertainty about the true system state is shown to be equivalent to an optimization over nested prediction sets, connecting to the literature on conformal prediction and extending prior art on single-level risk-averse decision making.
Analog over-the-air model aggregation can match the theoretical $\mathcal{O}(1/\sqrt{T})$ convergence of ideal FedAvg without requiring instantaneous channel state information or strict phase alignment.
TSDS cuts edge reasoning compute by up to 73% while ensuring reliable decision-making through smart deferral to cloud models.
