Anomaly detection plays a vital role in ensuring the security and reliability of edge clouds, which are decentralized computing environments with limited resources. However, the unique challenges of limited computing power and lack of edge-related labeled training data pose significant obstacles to effective supervised anomaly detection. In this paper, we propose an innovative approach that leverages transfer learning to address the lack of relevant labeled data and knowledge distillation to increase computational efficiency and achieve accurate anomaly detection on edge clouds. Our approach exploits transfer learning by utilizing knowledge from a pre-trained model and adapting it for anomaly detection on edge clouds. This enables the model to benefit from the learned features and patterns from related tasks such as network intrusion detection, resulting in improved detection accuracy. Additionally, we utilize knowledge distillation to distill the knowledge from the previously mentioned high-capacity model, known as the teacher model, into a more compact student model. This distillation process enhances the student model's computational efficiency while retaining its detection power. Evaluations conducted on our developed real-world edge cloud testbed show that, with the same amount of edge cloud's labeled dataset, our approach maintains high accuracy while significantly reducing the model's detection time to almost half for non-sequential models, from 81.11μs to 44.34μs on average. For sequential models, it reduces the detection time to nearly a third of the baseline model's, from 331.54μs to 113.86μs on average. These improvements make our approach exceptionally practical for real-time anomaly detection on edge clouds.