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FedQoS is proposed, a novel asynchronous, event-triggered FL framework that decouples local computation from global communication via a two-phase gating mechanism that achieves competitive personalized accuracy with only marginal performance loss compared to FedAvg, while substantially reducing QoS violations.
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.
