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This document presents a novel federated learning architecture for real-time detection of financial API attacks, addressing limitations of traditional centralized fraud detection systems. The proposed system combines differential privacy techniques and cross-institutional collaboration, achieving significant improvements in detection accuracy (from 86.8% to 93.2%), processing latency (under 10 ms), and a low false positive rate (2.3%). Experimental results demonstrate the system

Author
shiamrahan
Language
EN

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