Abstract: Federated Graph learning (FGL) for networked system data enables collaborative model training without sharing raw data, but it also introduces severe privacy risks. Model inversion attacks ...
Abstract: Traditional optimization-based techniques for time-synchronized state estimation (SE) often suffer from high online computational burden, limited phasor measurement unit (PMU) coverage, and ...
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