Deploying the low-level agent (LRA) on mobile devices presents several challenges and limitations, primarily related to computational resources, latency, and data privacy. Mobile devices typically have limited processing power and memory compared to cloud servers, which can hinder the performance of complex models like those used in mccHRL. To address this, model compression techniques, such as pruning and quantization, can be employed to reduce the model size and computational requirements without significantly sacrificing performance.
Another challenge is the latency associated with data transmission between the mobile device and the cloud. This can lead to delays in user feedback processing, impacting the responsiveness of the recommendation system. To mitigate this, edge computing can be utilized to perform more computations locally on the device, thereby reducing the need for frequent communication with the cloud. Additionally, implementing asynchronous updates can allow the LRA to continue making recommendations based on the most recent data while waiting for cloud responses.
Data privacy is also a significant concern, as sensitive user information may be processed on mobile devices. To enhance privacy, techniques such as federated learning can be adopted, allowing the LRA to learn from user data locally without transferring it to the cloud. This approach not only protects user privacy but also reduces the amount of data that needs to be transmitted, further alleviating latency issues.