Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks
This work addresses video caching optimization for wireless network operators to reduce backhaul traffic, but it is incremental as it builds on existing federated learning methods with resource-aware adaptations.
The paper tackles the problem of predicting users' future video content requests in wireless networks under privacy and resource constraints, proposing a resource-aware hierarchical federated learning solution that significantly improves prediction accuracy and reduces total energy expenditure compared to baselines.
Video caching can significantly improve backhaul traffic congestion by locally storing the popular content that users frequently request. A privacy-preserving method is desirable to learn how users' demands change over time. As such, this paper proposes a novel resource-aware hierarchical federated learning (RawHFL) solution to predict users' future content requests under the realistic assumptions that content requests are sporadic and users' datasets can only be updated based on the requested content's information. Considering a partial client participation case, we first derive the upper bound of the global gradient norm that depends on the clients' local training rounds and the successful reception of their accumulated gradients over the wireless links. Under delay, energy and radio resource constraints, we then optimize client selection and their local rounds and central processing unit (CPU) frequencies to minimize a weighted utility function that facilitates RawHFL's convergence in an energy-efficient way. Our simulation results show that the proposed solution significantly outperforms the considered baselines in terms of prediction accuracy and total energy expenditure.