Scheduling Algorithms for Federated Learning With Minimal Energy Consumption

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...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 34; no. 4; pp. 1215 - 1226
Main Author: Pilla, Laercio Lima
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Algorithms
Artificial Intelligence
Biological system modeling
Carbon dioxide
Computational modeling
Computer Science
Cost function
Costs
Data models
Data Structures and Algorithms
Distributed, Parallel, and Cluster Computing
Dynamic programming
Electronic devices
energy conservation
Energy consumption
Federated learning
knapsack problems
Machine learning
Optimization
parallel processing
Polynomials
Scheduling
Task analysis
Training
algorithms
optimization
scheduling
energy conservation
machine learning
parallel processing
knapsack problems
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary: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.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2023.3240833