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GRAB-FL is proposed, a graph-aware, Byzantine-resilient FL framework for a bounded gray-box setting in which adversaries may observe global model trajectories and adapt their updates over time but cannot inspect server-side trust states.
These findings challenge the prevailing view of deep learning dominance in time-series forecasting and establish new accuracy benchmarks for electricity load prediction.
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