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Tactic: Consider Federated Learning
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: green-ml-enabled-systems
Title
Consider Federated Learning
Description
Federated learning (FL) is a machine learning approach that aims to train a shared ML model on decentralized devices. Instead of sending raw data to a central server, FL trains the model directly on the devices where the data is generated, such as mobile phones or edge devices. Only the trained data or updated model parameters are then sent to a central server. Federated learning decreases the resources needed for transferring large amounts of data to a central server, which results in improved energy efficiency.
Participant
Software Designer
Related software artifact
Decentralized Device
Context
Machine Learning
Software feature
Model Training
Tactic intent
Improve energy efficiency by applying federated learning to minimize data transfers, if applicable
Target quality attribute
Energy Efficiency
Other related quality attributes
Accuracy
Measured impact
< unknown >
Source
Minsu Kim, Walid Saad, Mohammad Mozaffari, and Merouane Debbah. 2021. On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks. In ICC 2022 - IEEE International Conference on Communications. 2194–2199. (DOI: https://doi.org/10.1109/ICC45855.2022.9838362)Graphical representation
