API overview

In this page you can find the list of modules/submodules defined in fluke with their classes and functions.

fluke

Classes

DDict	A dictionary that can be accessed with dot notation recursively.
GlobalSettings	Global settings for fluke.
ObserverSubject	Subject class for the observer pattern.
Singleton	This metaclass is used to create singleton classes.

fluke.data

Classes

DataContainer	Container for train and test data.
DummyDataContainer	DataContainer designed for those datasets with a fixed data assignments, e.g., FEMNIST, Shakespeare and FCUBE.
FastDataLoader	A DataLoader-like object for a set of tensors that can be much faster than TensorDataset + DataLoader because dataloader grabs individual indices of the dataset and calls cat (slow).
DataSplitter	Utility class for splitting the data across clients.

Submodules

datasets	This module contains the Datasets for loading the supported datasets.
support	This module contains utility classes for loading datasets.

fluke.client

Classes

Client	Base Client class.
PFLClient	Personalized Federated Learning client.

fluke.server

Classes

Server

Basic Server for Federated Learning.

fluke.comm

Classes

Message	This class represents a message that can be exchanged between clients and the server.
Channel	A bi-directional communication channel.
ChannelObserver	Channel observer interface for the Observer pattern.

fluke.nets

Classes

EncoderHeadNet	Encoder (aka backbone) + Head Network [Base Class] This type of networks are defined as two subnetworks, where one is meant to be the encoder/backbone network that learns a latent representation of the input, and the head network that is the classifier part of the model.
GlobalLocalNet	Global-Local Network (Abstract Class).
HeadGlobalEncoderLocalNet	This implementation of the Global-Local Network (GlobalLocalNet) is meant to be used with the Encoder-Head architecture.
EncoderGlobalHeadLocalNet	This implementation of the Global-Local Network (GlobalLocalNet) is meant to be used with the Encoder-Head architecture.
MNIST_2NN	Multi-layer Perceptron for MNIST.
MNIST_CNN	Convolutional Neural Network for MNIST.
FedBN_CNN	Convolutional Neural Network with Batch Normalization for CIFAR-10.
MNIST_LR	Logistic Regression for MNIST.
CifarConv2	Convolutional Neural Network for CIFAR-10.
ResNet9	ResNet-9 network for CIFAR-100 classification.
FEMNIST_CNN	Convolutional Neural Network for FEMNIST.
VGG9	VGG-9 network for FEMNIST classification.
ResNet18	ResNet-18 network as defined in the torchvision library.
ResNet34	ResNet-34 network as defined in the torchvision library.
ResNet50	ResNet-50 network as defined in the torchvision library.
ResNet18GN	ResNet-18 network as defined in the torchvision library but with Group Normalization layers instead of Batch Normalization.
MoonCNN	Convolutional Neural Network for CIFAR-10.

fluke.utils

Classes

OptimizerConfigurator	This class is used to configure the optimizer and the learning rate scheduler.
Configuration	Fluke configuration class.
ClientObserver	Client observer interface.
ServerObserver	Server observer interface.

Functions

import_module_from_str(name)	Import a module from its name.
get_class_from_str(module_name, class_name)	Get a class from its name.
get_loss(lname)	Get a loss function from its name.
get_model(mname, **kwargs)	Get a model from its name.
get_scheduler(sname)	Get a learning rate scheduler from its name.
clear_cache([ipc])	Clear the CUDA cache.
get_full_classname(classtype)	Get the fully qualified name of a class.

Submodules

log	This submodule provides logging utilities.
model	This submodule provides utilities for pytorch model manipulation.
log	This submodule provides logging utilities.

fluke.evaluation

Classes

Evaluator	This class is the base class for all evaluators in fluke.
ClassificationEval	Evaluate a PyTorch model for classification.

fluke.algorithms

Classes

CentralizedFL	Centralized Federated Learning algorithm.
PersonalizedFL	Personalized Federated Learning algorithm.

Submodules

apfl	Implementation of the APFL [APFL20] algorithm.
ccvr	Implementation of the CCVR [CCVR21] algorithm.
ditto	Implementation of the DITTO [Ditto21] algorithm.
fedala	Implementation of the FedALA [FedALA23] algorithm.
fedamp	Implementation of the FedAMP [FedAMP21] algorithm.
fedavg	Implementation of the Federated Averaging [FedAVG17] algorithm.
fedavgm	Implementation of the Federated Averaging with momentum [FedAVGM19] algorithm.
fedaws	Implementation of the Federated Averaging with Spreadout [FedAwS20] algorithm.
fedbabu	Implementation of the Federated Averaging with Body Aggregation and Body Update [FedBABU22] algorithm.
fedbn	Implementation of the FedBN [FedBN21] algorithm.
feddyn	Implementation of the FedDyn [FedDyn21] algorithm.
fedexp	Implementation of the FedExP [FedExP23] algorithm.
fedhp	Implementation of the FedHP: Federated Learning with Hyperspherical Prototypical Regularization [FedHP24] algorithm.
fedlc	Implementation of the [FedLC22] algorithm.
fednh	Implementation of the [FedNH23] algorithm.
fednova	Implementation of the [FedNova21] algorithm.
fedopt	Implementation of the [FedOpt21] algorithm.
fedper	Implementation of the [FedPer19] algorithm.
fedproto	Implementation of the FedProto [FedProto22] algorithm.
fedprox	Implementation of the [FedProx18] algorithm.
fedrep	Implementation of the [FedRep21] algorithm.
fedrod	Implementation of the FedROD [FedROD22] algorithm.
fedrs	Implementation of the [FedRS21] algorithm.
fedsam	Implementation of the [FedSAM22] algorithm.
fedsgd	Implementation of the [FedSGD17] algorithm.
lg_fedavg	Implementation of the [LG-FedAVG20] algorithm.
moon	Implementation of the [Moon21] algorithm.
per_fedavg	Implementation of the [Per-FedAVG20] algorithm.
pfedme	Implementation of the [pFedMe20] algorithm.
scaffold	Implementation of the [SCAFFOLD20] algorithm.
superfed	Implementation of the [SuPerFed22] algorithm.