Type
Feature
Description
Paper
Personalized Cross-Silo Federated Learning on Non-IID Data
Link
https://doi.org/10.1609/aaai.v35i9.16960
Motivation about why the paper should be implemented as a baseline.
FedAMP is a similarity based personalized FL algorithm that aims to improve performance under heterogeneity. The paper shows improved performance when compared against standard algorithms like FedAVG or FedProx.
Plan is to implement FedAMP and evaluate its performance on CIFAR-10 dataset with pathological sampling and compare against FedAvg and FedProx. If the community is interested in this baseline, I would like to work on it and submit a PR.
Type
Feature
Description
Paper
Personalized Cross-Silo Federated Learning on Non-IID Data
Link
https://doi.org/10.1609/aaai.v35i9.16960
Motivation about why the paper should be implemented as a baseline.
FedAMP is a similarity based personalized FL algorithm that aims to improve performance under heterogeneity. The paper shows improved performance when compared against standard algorithms like FedAVG or FedProx.
Plan is to implement FedAMP and evaluate its performance on CIFAR-10 dataset with pathological sampling and compare against FedAvg and FedProx. If the community is interested in this baseline, I would like to work on it and submit a PR.