Abstract: Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management. While learned ...
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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 ...
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