Papers with distributed or federated learning

1 papers
TextHide: Tackling Data Privacy in Language Understanding Tasks (2020.findings-emnlp)

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Challenge: Unsolved privacy challenges in distributed or federated learning are a challenge for many domains including Natural Language Processing.
Approach: They propose a federated learning framework that adds an encryption step to prevent an eavesdropping attacker from recovering private text data.
Outcome: The proposed model can effectively defend against attacks on shared gradients or representations and the averaged accuracy reduction is only 1.9%.

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