ZexiLee/ICML-2023-FedLAW
The is the official implementation of ICML 2023 paper "Revisiting Weighted Aggregation in Federated Learning with Neural Networks".
When training machine learning models collaboratively across multiple organizations without sharing raw data, this project helps researchers and practitioners achieve better model accuracy. It takes individual models trained on local datasets and aggregates them into a more generalized global model. This is for machine learning researchers and data scientists who work with federated learning setups.
No commits in the last 6 months.
Use this if you are developing or experimenting with federated learning systems and want to improve the generalization performance of your global models.
Not ideal if you are looking for a plug-and-play solution for deploying a federated learning system without understanding the underlying aggregation algorithms.
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63
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9
Language
Python
License
MIT
Category
Last pushed
Aug 28, 2023
Commits (30d)
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