Summary of Neurips 2023 Competition: Privacy Preserving Federated Learning Document Vqa, by Marlon Tobaben et al.

NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA

by Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito, Khanh Nguyen, Raouf Kerkouche, Kangsoo Jung, Joonas Jälkö, Lei Kang, Andrey Barsky, Vincent Poulain d’Andecy, Aurélie Joseph, Aashiq Muhamed, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Llados, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas

First submitted to arxiv on: 6 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The PFL-DocVQA competition challenged researchers to develop privately and communication-efficient solutions for invoice processing using a pre-trained Document Visual Question Answering (DocVQA) model. The competition introduced a dataset of real invoice documents, questions, and answers requiring information extraction and reasoning over document images. Participants fine-tuned the provided DocVQA model for the new domain, mimicking a typical federated invoice processing setup. To protect sensitive information, participants proposed solutions reducing communication costs while maintaining a minimum utility threshold in Track 1 and protecting all information using differential privacy in Track 2. The competition served as a testbed for private federated learning methods and raised awareness about privacy within the document image analysis community.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The PFL-DocVQA competition is about developing new ways to process invoices while keeping important details private. It’s like a big puzzle where you need to figure out what’s in an invoice, but you can’t show anyone else. A special model was used to help with this task, and people had to make it better for the job. Some clever ideas were shared to keep sensitive information safe while still getting the important parts of the invoices. This competition helped scientists learn how to do these kinds of things in the future.