
FL-GUARD introduces a dynamic framework for detecting and recovering from Negative Federated Learning in real-time, ensuring improved performance and adaptability in federated learning systems.


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fl-guard-a-holistic-framework-for-run-time-detection-and-recovery-of-negative-federated-learning


FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning


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FL-GUARD introduces a dynamic solution for detecting and recovering from Negative Federated Learning in real-time, outperforming previous approaches.



The author introduces FL-GUARD, a dynamic solution for tackling Negative Federated Learning in run-time, ensuring recovery only when necessary. The framework outperforms previous approaches by detecting NFL quickly and efficiently.
