# Scheduling Algorithms for Federated Learning with Minimal Energy Consumption

arXiv: https://arxiv.org/abs/2209.06210 · HTML: https://mlsanity.com/paper/2209.06210/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [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)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [Federated Graph Classification over Non-IID Graphs](https://mlsanity.com/paper/2106.13423/) (2021)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [Cost-Effective Federated Learning Design](https://mlsanity.com/paper/2012.08336/) (2020)
- [Can Federated Learning Save The Planet?](https://mlsanity.com/paper/2010.06537/) (2020)
- [To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices](https://mlsanity.com/paper/2012.11804/) (2020)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning](https://mlsanity.com/paper/2107.08147/) (2021)
- [FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://mlsanity.com/paper/2006.07273/) (2020)
- [Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity](https://mlsanity.com/paper/2005.12326/) (2020)
- [Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1812.03633/) (2018)
- [From Server-Based to Client-Based Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/1909.08329/) (2019)
- [Optimal Task Assignment to Heterogeneous Federated Learning Devices](https://mlsanity.com/paper/2010.00239/) (2020)
- [Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systems](https://mlsanity.com/paper/2103.13293/) (2021)
- [Protea: Client Profiling within Federated Systems using Flower](https://mlsanity.com/paper/2207.01053/) (2022)

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

- [Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning](https://mlsanity.com/paper/2609.39250/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private.With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques).Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective.In this paper, we investigate the problem of minimizing the energy consumption of FL training on heterogeneous devices by controlling the workload distribution.We model this as the Minimal Cost FL Schedule problem, a total cost minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with arbitrary cost functions.We propose a pseudo-polynomial optimal solution to the problem based on the previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem.We also provide four algorithms for scenarios where cost functions are monotonically increasing and follow the same behavior.These solutions are likewise applicable on the minimization of other kinds of costs, and in other one-dimensional data partition problems.