Flower Baselines Documentation#

Welcome to Flower Baselines’ documentation. Flower is a friendly federated learning framework.

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Flower Baselines#

Flower Baselines are a collection of organised directories used to reproduce results from well-known publications or benchmarks. You can check which baselines already exist and/or contribute your own baseline.

Method

Dataset

Tags

dasha

cifar10, mushrooms, libsvm

compression, heterogeneous setting, variance reduction, image classification

depthfl

CIFAR-100

image classification, system heterogeneity, cross-device, knowledge distillation

fedavgm

CIFAR-10, Fashion-MNIST

non-iid, image classification

fedbn

MNIST, MNIST-M, SVHN, USPS, SynthDigits

data heterogeneity, feature shift, cross-silo

fedmeta

FEMNIST, SHAKESPEARE

meta learning, maml, meta-sgd, personalization

fedmlb

CIFAR-100, Tiny-ImageNet

data heterogeneity, knowledge distillation, image classification

fednova

CIFAR-10

normalized averaging, heterogeneous optimization, image classification

fedpara

CIFAR-10, CIFAR-100, MNIST

image classification, personalization, low-rank training, tensor decomposition

fedper

CIFAR-10, FLICKR-AES

system heterogeneity, image classification, personalization, horizontal data partition

fedprox

MNIST

image classification, cross-device, stragglers

fedstar

Ambient Context, Speech Commands

Audio Classification, Semi Supervised learning

fedvssl

UCF-101, Kinectics-400

action recognition, cross-device, ssl, video, videossl

fedwav2vec2

TED-LIUM 3

speech, asr, cross-device

fjord

“CIFAR-10”

“Federated Learning”, “Heterogeneity”, “Efficient DNNs”, “Distributed Systems”

heterofl

MNIST, CIFAR-10

system heterogeneity, image classification

hfedxgboost

a9a, cod-rna, ijcnn1, space_ga, cpusmall, YearPredictionMSD

cross-silo, tree-based, XGBoost, Classification, Regression, Tabular

moon

CIFAR-10, CIFAR-100

data heterogeneity, image classification, cross-silo, constrastive-learning

niid_bench

CIFAR-10, MNIST, Fashion-MNIST

data heterogeneity, image classification, benchmark

tamuna

MNIST

local training, communication compression, partial participation, variance reduction

Tutorials#

A learning-oriented series of tutorials, the best place to start.

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Coming soon

How-to guides#

Problem-oriented how-to guides show step-by-step how to achieve a specific goal.

Explanations#

Understanding-oriented concept guides explain and discuss key topics and underlying ideas behind Flower and collaborative AI.

Note

Coming soon

References#

Information-oriented API reference and other reference material.