Federated Learning (FL) systems are vulnerable to adversarial attacks, such as model poisoning and backdoor attacks. However, existing defense mechanisms often fall short in real-world settings due to key limitations: they may rely on impractical assumptions, introduce distortions by modifying aggregation functions, or degrade model performance even in benign scenarios. To address these issues, we propose a novel anomaly detection method designed specifically for practical FL scenarios. Our approach employs a two-stage, conditionally activated detection mechanism: cross-round check first detects whether suspicious activity has occurred, and, if warranted, a cross-client check filters out malicious participants. This mechanism preserves utility while avoiding unrealistic assumptions. Moreover, to ensure the transparency and integrity of the defense mechanism, we incorporate zero-knowledge proofs, enabling clients to verify the detection without relying solely on the server's goodwill. To the best of our knowledge, this is the first method to bridge the gap between theoretical advances in FL security and the demands of real-world deployment. Extensive experiments across diverse tasks and real-world edge devices demonstrate the effectiveness of our method over state-of-the-art defenses.