## 6 Conclusions and Future Work  
This paper presented Edge-FLGuard+, a lightweight and privacy-preserving federated learning framework designed for real-time anomaly detection in 5G-enabled smart home IoT environments. The proposed system integrates a GRU-based autoencoder architecture with the FedAvg protocol, enabling decentralized, low-latency, and communication-efficient training across edge nodes. Experimental results on filtered subsets of the TON_IoT and CIC-IDS2018 datasets demonstrate that Edge-FLGuard+ achieves a favorable balance between detection accuracy (\(\geq\)96%), communication overhead (\(\approx\)2.1 MB/round), and inference latency (<7 ms). Comparative analysis against centralized and local-only baselines confirms its competitiveness, while ablation studies validate the benefits of pruning and quantization for deployment on resource-constrained devices. These results confirm that federated learning frameworks are viable for enhancing cybersecurity in smart home settings, where privacy, real-time responsiveness, and device heterogeneity are key concerns. However, this work also has several limitations that must be acknowledged. The current framework assumes stable and low-latency 5G connectivity, which may not be universally available across all smart home contexts. Furthermore, while the system is designed to support heterogeneous edge devices, real-world deployment could face  
Figure 5: Accuracy vs. communication overhead for each Edge-FLGuard+ configuration. Each point represents a single measurement based on final model performance under the specified compression or privacy scheme. challenges in calibrating model performance across widely varying hardware capabilities. Finally, although our evaluation uses realistic datasets, field testing in live home environments remains a necessary step for broader validation. Additionally, while key performance metrics such as accuracy and communication overhead were reported, our current evaluation reflects single-run results per configuration. As such, we were unable to generate statistical visualizations (e.g., box plots) to capture variability across multiple runs--an aspect we aim to address in future experimentation. Future WorkFuture research will explore the following directions to extend the applicability and robustness of the Edge-FLGuard+ framework:  
* Cross-Domain Generalization: Investigate transferability of trained models across heterogeneous environments (e.g., smart buildings, smart healthcare). * Adaptive and Personalized FL Models: Incorporate continual learning and attention-based mechanisms to adapt to evolving device behavior patterns. * LLM-Integrated Threat Intelligence: Explore the use of large language models (LLMs) for contextual reasoning, anomaly explanation, and threat attribution at the edge. * Adaptive Monitoring Integration: Future versions of Edge-FLGuard+ could incorporate adaptive data collection and monitoring strategies (e.g., dynamic sampling, context-aware feedback) to further reduce communication and computation costs without sacrificing detection accuracy. * 5G-Aware Anomaly Detection: Future versions of the framework could incorporate 5G-specific network telemetry (e.g., signal strength, latency spikes, handover frequency) as auxiliary features for anomaly detection. This would enable context-aware threat analysis and potentially improve detection accuracy and responsiveness in highly dynamic network conditions. Overall, this work lays the foundation for intelligent, secure, and scalable anomaly detection in next-generation smart home ecosystems. **Funding:** This research received no external funding.