# Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks

arXiv: https://arxiv.org/abs/2107.03520 · HTML: https://mlsanity.com/paper/2107.03520/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)
- [Energy-Efficient Radio Resource Allocation for Federated Edge Learning](https://mlsanity.com/paper/1907.06040/) (2019)
- [Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning](https://mlsanity.com/paper/2106.06796/) (2021)
- [Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM](https://mlsanity.com/paper/2009.06459/) (2020)
- [Lightwave Power Transfer for Federated Learning-based Wireless Networks](https://mlsanity.com/paper/2005.03977/) (2020)

## Built on by (most cited first, 3 of 3)

- [Green Federated Learning: A new era of Green Aware AI](https://mlsanity.com/paper/2409.12626/) (2024)
- [Adaptive Federated Pruning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2305.09042/) (2023)
- [Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission](https://mlsanity.com/paper/2206.01373/) (2022)

## Abstract (arXiv)

We investigate resource allocation scheme to reduce the energy consumption of federated learning (FL) in the integrated fog-cloud computing enabled Internet-of-things (IoT) networks. In the envisioned system, IoT devices are connected with the centralized cloud server (CS) via multiple fog access points (F-APs). We consider two different scenarios for training the local models. In the first scenario, local models are trained at the IoT devices and the F-APs upload the local model parameters to the CS. In the second scenario, local models are trained at the F-APs based on the collected data from the IoT devices and the F-APs collaborate with the CS for updating the model parameters. Our objective is to minimize the overall energy-consumption of both scenarios subject to FL time constraint. Towards this goal, we devise a joint optimization of scheduling of IoT devices with the F-APs, transmit power allocation, computation frequency allocation at the devices and F-APs and decouple it into two subproblems. In the first subproblem, we optimize the IoT device scheduling and power allocation, while in the second subproblem, we optimize the computation frequency allocation. For each scenario, we develop a conflict graph based solution to iteratively solve the two subproblems. Simulation results show that the proposed two schemes achieve a considerable performance gain in terms of the energy consumption minimization. The presented simulation results interestingly reveal that for a large number of IoT devices and large data sizes, it is more energy efficient to train the local models at the IoT devices instead of the F-APs.