Authors:
Daniel Perazzo
1
;
Thiago de Souza
1
;
Pietro Masur
1
;
Eduardo de Amorim
1
;
Pedro de Oliveira
1
;
Kelvin Cunha
1
;
Lucas Maggi
1
;
Francisco Simões
1
;
2
;
Veronica Teichrieb
1
and
Lucas Kirsten
3
Affiliations:
1
Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Recife/PE, Brazil
;
2
Visual Computing Lab, Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife/PE, Brazil
;
3
HP Inc., Porto Alegre/RS, Brazil
Keyword(s):
Federated Learning, Document Analysis, Privacy, Dataset.
Abstract:
Data privacy has recently become one of the main concerns for society and machine learning researchers. The question of privacy led to research in privacy-aware machine learning and, amongst many other techniques, one solution gaining ground is federated learning. In this machine learning paradigm, data does not leave the user’s device, with training happening on it and aggregated in a remote server. In this work, we present, to our knowledge, the first federated dataset for document classification: FedBID. To demonstrate how this dataset can be used for evaluating different techniques, we also developed a system, FedDocs, for federated learning for document classification. We demonstrate the characteristics of our federated dataset, along with different types of distributions possible to be created with our dataset. Finally, we analyze our system, FedDocs, in our dataset, FedBID, in multiple different scenarios. We analyze a federated setting with balanced categories, a federated se
tting with unbalanced classes, and, finally, simulating a siloed federated training. We demonstrate that FedBID can be used to analyze a federated learning algorithm. Finally, we hope the FedBID dataset allows more research in federated document classification. The dataset is available in https://github.com/voxarlabs/FedBID.
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