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SecureDrive-FL withstands Man-in-the-Middle attacks without sacrificing accuracy, achieving a remarkable balance of privacy and performance in federated learning.
Achieving an F1 score of 0.69, this framework adapts to evolving operational conditions in connected vehicles, outperforming traditional methods and demonstrating resilience against concept drift.
