# Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks

arXiv: https://arxiv.org/abs/2311.06918 · HTML: https://mlsanity.com/paper/2311.06918/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD](https://mlsanity.com/paper/2010.12998/) (2020)
- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity](https://mlsanity.com/paper/2308.01562/) (2023)
- [Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions](https://mlsanity.com/paper/1810.13287/) (2018)
- [Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles](https://mlsanity.com/paper/2205.09529/) (2022)

## Built on by (most cited first, 1 of 1)

- [Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks](https://mlsanity.com/paper/2402.04216/) (2024)

## Abstract (arXiv)

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.