FS-TFP/federatedscope/core/data

DataZoo

FederatedScope provides a rich collection of federated datasets for researchers, including images, texts, graphs, recommendation systems, and speeches, as well as utility classes BaseDataTranslator for building your own FS datasets.

Built-in FS data

All datasets can be accessed from federatedscope.core.auxiliaries.data_builder.get_data, which are built to federatedscope.core.data.StandaloneDataDict (for more details, see [DataZoo advanced]). By setting cfg.data.type = DATASET_NAME, FS would download and pre-process a specific dataset to be passed to FedRunner. For example:

# Source: federatedscope/main.py

data, cfg = get_data(cfg)
runner = FedRunner(data=data,
                   server_class=get_server_cls(cfg),
                   client_class=get_client_cls(cfg),
                   config=cfg.clone())

We provide a look-up table for you to get started with our DataZoo:

cfg.data.type	Domain
FEMNIST	CV
Celeba	CV
{DNAME}@torchvision	CV
Shakespeare	NLP
SubReddit	NLP
Twitter (Sentiment140)	NLP
{DNAME}@torchtext	NLP
{DNAME}@huggingface_datasets	NLP
Cora	Graph (node-level)
CiteSeer	Graph (node-level)
PubMed	Graph (node-level)
DBLP_conf	Graph (node-level)
DBLP_org	Graph (node-level)
csbm	Graph (node-level)
Epinions	Graph (link-level)
Ciao	Graph (link-level)
FB15k	Graph (link-level)
FB15k-237	Graph (link-level)
WN18	Graph (link-level)
MUTAG	Graph (graph-level)
BZR	Graph (graph-level)
COX2	Graph (graph-level)
DHFR	Graph (graph-level)
PTC_MR	Graph (graph-level)
AIDS	Graph (graph-level)
NCI1	Graph (graph-level)
ENZYMES	Graph (graph-level)
DD	Graph (graph-level)
PROTEINS	Graph (graph-level)
COLLAB	Graph (graph-level)
IMDB-BINARY	Graph (graph-level)
IMDB-MULTI	Graph (graph-level)
REDDIT-BINARY	Graph (graph-level)
HIV	Graph (graph-level)
ESOL	Graph (graph-level)
FREESOLV	Graph (graph-level)
LIPO	Graph (graph-level)
PCBA	Graph (graph-level)
MUV	Graph (graph-level)
BACE	Graph (graph-level)
BBBP	Graph (graph-level)
TOX21	Graph (graph-level)
TOXCAST	Graph (graph-level)
SIDER	Graph (graph-level)
CLINTOX	Graph (graph-level)
graph_multi_domain_mol	Graph (graph-level)
graph_multi_domain_small	Graph (graph-level)
graph_multi_domain_biochem	Graph (graph-level)
cikmcup	Graph (graph-level)
toy	Tabular
synthetic	Tabular
quadratic	Tabular
{DNAME}openml	Tabular
vertical_fl_data	Tabular(vertical)
VFLMovieLens1M	Recommendation
VFLMovieLens10M	Recommendation
HFLMovieLens1M	Recommendation
HFLMovieLens10M	Recommendation
VFLNetflix	Recommendation
HFLNetflix	Recommendation

DataZoo Advanced

In this section, we will introduce key concepts and tools to help you understand how FS data works and how to use it to build your own data in FS.

Concepts:

• federatedscope.core.data.ClientData

  • ClientData is a subclass of dict. In federated learning, each client (server) owns a ClientData for training, validating, or testing. Thus, each ClientData has one or more of train, val, and test as keys, and DataLoader accordingly.

  • The DataLoader of each key is created by setup() method, which specifies the arguments of DataLoader, such as batch_size, shuffle of cfg.

    Example:

    # Instantiate client_data for each Client
    client_data = ClientData(DataLoader, 
                             cfg, 
                             train=train_data, 
                             val=None, 
                             test=test_data)
    # other_cfg with different batch size
    client_data.setup(other_cfg)
    print(client_data)
    
    >> {'train': DataLoader(train_data), 'test': DataLoader(test_data)}
    

• federatedscope.core.data.StandaloneDataDict

  • StandaloneDataDict is a subclass of dict. As the name implies, StandaloneDataDict consists of all ClientData with client index as key (0, 1, 2, ...) in standalone mode. The key 0 is the data of the server for global evaluation or other usages.
  • The method preprocess() in StandaloneDataDict makes changes to inner ClientData when cfg changes, such as in global mode, we set cfg.federate.method == "global", and StandaloneDataDict will merge all ClientData to one client to perform global training.

Tools

• federatedscope.core.data.BaseDataTranslator

  • BaseDataTranslator converts torch.utils.data.Dataset or dict of data split to StandaloneDataDict according to cfg. After translating, it can be directly passed to FedRunner to launch a FL course.

  • BaseDataTranslator will split data to train, val, and test by cfg.data.splits (ML split). And using Splitter to split each data split to each client (FL split). In order to use BaseDataTranslator, cfg.data.splitter, cfg.federate.client_num, and other arguments of Splitter must be specified.

  Example:

  cfg.data.splitter = 'lda'
  cfg.federate.client_num = 5
  cfg.data.splitter_args = [{'alpha': 0.2}]
  
  translator = BaseDataTranslator(global_cfg, DataLoader)
  raw_data = CIFAR10()
  fs_data = translator(raw_data)
  
  runner = FedRunner(data=fs_data,
                     server_class=get_server_cls(cfg),
                     client_class=get_client_cls(cfg),
                     config=cfg.clone())
  

• federatedscope.core.splitters

  • To generate simulated federation datasets, we provide splitter who are responsible for dispersing a given standalone dataset into multiple clients, with configurable statistical heterogeneity among them.

  We provide a look-up table for you to get started with our Splitter:

  cfg.data.splitter	Domain	Arguments
  LDA	Generic	alpha
  Louvain	Graph (node-level)	delta
  Random	Graph (node-level)	sampling_rate, overlapping_rate, drop_edge
  rel_type	Graph (link-level)	alpha
  Scaffold	Molecular	-
  Scaffold_lda	Molecular	alpha
  Rand_chunk	Graph (graph-level)	-