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A federated learning framework for Network Intrusion Detection Systems (NIDS) that incorporates Byzantine-tolerant aggregation strategies to mitigate attacks from malicious clients.
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This project is a thesis-driven implementation of existing federated learning algorithms (Krum, Bulyan) applied to standard cybersecurity datasets (CIC-IDS2017). While technically sound for its academic purpose, it has minimal adoption (3 stars), no community velocity, and lacks unique intellectual property that would prevent a competitor from recreating the logic using standard FL frameworks like Flower or OpenFL.
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