## 7 Open Challenges and Future Research Opportunities
Future research should focus on creating efficient task offloading strategies that minimize latency and maximize the utilization of edge and cloud resources [31; 35]. Developing edge-centric training techniques that focus on maximizing the training capabilities of edge devices while minimizing dependency on cloud resources can also be beneficial. These techniques can enhance the autonomy and efficiency of edge networks, ensuring robust performance even with limited cloud interactions. Memory limitations: Quantization is a method used to address memory limitations by lowering memory usage and enabling efficient computation on edge devices. However, this can potentially reduce model precision and affect performance. Future research could enhance post-training quantization methods to maintain a higher model accuracy while reducing memory usage [11; 28]. Another promising area is mixed precision training, where lower precisions are used during less-critical training phases and higher precisions are used for essential computations. This balance can help optimize performance and memory efficiency, making deploying sophisticated models on resource-constrained edge devices feasible. Local processing risks: Hybrid processing ensures real-time processing, reduces latency, is resource-intensive, and requires sophisticated architecture. Future research should focus on developing optimized hybrid architectures that efficiently distribute processing tasks between edge and cloud-based methods based on real-time requirements and resource availability [20; 35]. Implementing dynamic task allocation algorithms that can adapt to changing network conditions and processing loads is also crucial. These algorithms can ensure optimal performance and resource utilization, providing a balanced approach to processing that leverages the strengths of edge and cloud resources. Data privacy: Edge-cloud collaborations balance the load between the edge and cloud, enhancing data safety but potentially introducing latency issues and requiring robust communication channels. Enhancing secure multi-party computation methods can ensure data privacy during collaborative processing between edge and cloud processes [28; 39]. Leveraging blockchain technology to create secure and immutable records of data transactions between edge devices and the cloud can enhance overall security. This integration can provide a decentralized and transparent approach to data management, reducing the risk of data breaches and ensuring the integrity of data exchanges. Ethical concerns: The integration of AI in EC-IoT raises significant ethical concerns due to the quantity of data these networks handle. These concerns must be carefully assessed and addressed to ensure responsible and fair use of technology. One primary ethical issue is the potential for bias and unfairness in AI algorithms, which can perpetuate or amplify existing biases in the data, leading to discriminatory outcomes [1; 2]. Guaranteeing transparency and accountability in AI-based decision-making processes is also essential; stakeholders must be able to understand and audit the decisions made by these systems to foster trust and accountability [5]. Additionally, informed consent is essential for maintaining user trust and compliance with data protection regulations, requiring clear communication to users about the data being collected and their intended use [6]. Ethical guidelines must prioritize user well-being, avoid harm, and respect user autonomy to ensure that AI technologies contribute positively to society [7]. Addressing these ethical concerns is vital for the responsible deployment of AI in EC-IoT systems, guiding future research toward developing fair, transparent, and accountable AI frameworks [8].