omarfoq/FedEM
Official code for "Federated Multi-Task Learning under a Mixture of Distributions" (NeurIPS'21)
This project helps machine learning practitioners develop better personalized models for users whose data is stored across many devices and cannot be centrally collected. It takes decentralized datasets (like those from smartphones or IoT devices) as input and outputs custom machine learning models that perform well for each individual client, even if their data patterns are unique. This is for machine learning researchers and engineers working with federated learning setups where data privacy or distribution prevents centralized training.
167 stars. No commits in the last 6 months.
Use this if you need to train personalized machine learning models across many devices without sharing raw data, and existing federated learning methods struggle with diverse user data.
Not ideal if your data can be aggregated into a single location for model training, or if you only need a single model that works for the average user.
Stars
167
Forks
28
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 07, 2022
Commits (30d)
0
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