# Energy-Efficient Multi-Orchestrator Mobile Edge Learning

arXiv: https://arxiv.org/abs/2109.00757 · HTML: https://mlsanity.com/paper/2109.00757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 2 Sep 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)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2002.11343/) (2020)
- [Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design](https://mlsanity.com/paper/2003.00199/) (2020)
- [Communicate to Learn at the Edge](https://mlsanity.com/paper/2009.13269/) (2020)
- [D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge](https://mlsanity.com/paper/2001.11342/) (2020)
- [Path Finding I :Solving Linear Programs with ~O(sqrt(rank)) Linear System Solves](https://mlsanity.com/paper/1312.6677/) (2013)
- [Adaptive Task Allocation for Mobile Edge Learning](https://mlsanity.com/paper/1811.03748/) (2018)
- [Jointly Optimizing Dataset Size and Local Updates in Heterogeneous Mobile Edge Learning](https://mlsanity.com/paper/2006.07402/) (2020)

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

- [Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2109.12409/) (2021)

## Abstract (arXiv)

Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g., IoT devices). In MEL, possible coexistence of multiple learning tasks with different datasets may arise. The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation. To this end, we aim to develop an energy-efficient framework for learners-orchestrator association and learning task allocation, in which each orchestrator gets associated with a group of learners with the same learning task based on their communication channel qualities and computational resources, and allocate the tasks accordingly. Therein, a multi objective optimization problem is formulated to minimize the total energy consumption and maximize the learning tasks' accuracy. However, solving such optimization problem requires centralization and the presence of the whole environment information at a single entity, which becomes impractical in large-scale systems. To reduce the solution complexity and to enable solution decentralization, we propose lightweight heuristic algorithms that can achieve near-optimal performance and facilitate the trade-offs between energy consumption, accuracy, and solution complexity. Simulation results show that the proposed approaches reduce the energy consumption significantly while executing multiple learning tasks compared to recent state-of-the-art methods.