With the extensive adoption of edge computing and IoT infrastructure, the vulnerability landscape has expanded significantly along with stringent constraints concerning computation, energy efficiency, and data privacy. Traditional centralized IDS solutions tend to be less than ideal for such conditions, as they are highly dependent on centralized data labeling, large-scale computation, and constant traffic sharing. This paper presents FedSSL-IDS, a novel privacy-preserving IDS framework leveraging Federated Learning (FL) and Self-Supervised Learning (SSL), specifically designed for the needs of edge-based network architectures. The solution applies autoencoder-based self-supervised learning to extract informative latent feature representations of unlabeled network traffic, after which federated learning is performed on the lightweight classifier with supervised learning without any raw data sharing. In order to facilitate the implementation of the system on resource-limited edge devices, the system employs advanced model optimization methods, such as magnitude-based pruning and post-training quantization. Performance evaluations of the FedSSL-IDS framework were conducted using the CICIDS2017 dataset in a simulated federated edge environment with class-imbalanced and non-IID client distributions. According to the experimental results, the full precision model reached an average detection accuracy of 96.90% across all classes, whereas the major attack classes, like DDoS and PortScan, achieved impressive class-wise accuracy rates. Moreover, the combination of pruning and FP16 quantization greatly decreases the size of the model and computational cost during inference without compromising its near-native accuracy in detecting intrusions. Nevertheless, aggressive INT8 quantization leads to a substantial reduction in the detection performance of rare classes of attacks such as SQL injection attacks, showing that a compromise must be made between efficient compression and reliable detection in edge scenarios. Even though the presented framework increases the privacy level since there is no raw traffic exchange in federated training, sophisticated privacy-preserving techniques like differential privacy and secure aggregation are not part of the current design.