Pub Date : 2021-06-01DOI: 10.18653/V1/2021.PRIVATENLP-1.1
Om Thakkar, Swaroop Indra Ramaswamy, Rajiv Mathews, F. Beaufays
Recent works have shown that language models (LMs), e.g., for next word prediction (NWP), have a tendency to memorize rare or unique sequences in the training data. Since useful LMs are often trained on sensitive data, it is critical to identify and mitigate such unintended memorization. Federated Learning (FL) has emerged as a novel framework for large-scale distributed learning tasks. It differs in many aspects from the well-studied central learning setting where all the data is stored at the central server, and minibatch stochastic gradient descent is used to conduct training. This work is motivated by our observation that NWP models trained under FL exhibited remarkably less propensity to such memorization compared to the central learning setting. Thus, we initiate a formal study to understand the effect of different components of FL on unintended memorization in trained NWP models. Our results show that several differing components of FL play an important role in reducing unintended memorization. First, we discover that the clustering of data according to users—which happens by design in FL—has the most significant effect in reducing such memorization. Using the Federated Averaging optimizer with larger effective minibatch sizes for training causes a further reduction. We also demonstrate that training in FL with a user-level differential privacy guarantee results in models that can provide high utility while being resilient to memorizing out-of-distribution phrases with thousands of insertions across over a hundred users in the training set.
{"title":"Understanding Unintended Memorization in Language Models Under Federated Learning","authors":"Om Thakkar, Swaroop Indra Ramaswamy, Rajiv Mathews, F. Beaufays","doi":"10.18653/V1/2021.PRIVATENLP-1.1","DOIUrl":"https://doi.org/10.18653/V1/2021.PRIVATENLP-1.1","url":null,"abstract":"Recent works have shown that language models (LMs), e.g., for next word prediction (NWP), have a tendency to memorize rare or unique sequences in the training data. Since useful LMs are often trained on sensitive data, it is critical to identify and mitigate such unintended memorization. Federated Learning (FL) has emerged as a novel framework for large-scale distributed learning tasks. It differs in many aspects from the well-studied central learning setting where all the data is stored at the central server, and minibatch stochastic gradient descent is used to conduct training. This work is motivated by our observation that NWP models trained under FL exhibited remarkably less propensity to such memorization compared to the central learning setting. Thus, we initiate a formal study to understand the effect of different components of FL on unintended memorization in trained NWP models. Our results show that several differing components of FL play an important role in reducing unintended memorization. First, we discover that the clustering of data according to users—which happens by design in FL—has the most significant effect in reducing such memorization. Using the Federated Averaging optimizer with larger effective minibatch sizes for training causes a further reduction. We also demonstrate that training in FL with a user-level differential privacy guarantee results in models that can provide high utility while being resilient to memorizing out-of-distribution phrases with thousands of insertions across over a hundred users in the training set.","PeriodicalId":270632,"journal":{"name":"Proceedings of the Third Workshop on Privacy in Natural Language Processing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122990122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.18653/V1/2021.PRIVATENLP-1.4
Abhik Jana, Chris Biemann
To build machine learning-based applications for sensitive domains like medical, legal, etc. where the digitized text contains private information, anonymization of text is required for preserving privacy. Sequence tagging, e.g. as done in Named Entity Recognition (NER) can help to detect private information. However, to train sequence tagging models, a sufficient amount of labeled data are required but for privacy-sensitive domains, such labeled data also can not be shared directly. In this paper, we investigate the applicability of a privacy-preserving framework for sequence tagging tasks, specifically NER. Hence, we analyze a framework for the NER task, which incorporates two levels of privacy protection. Firstly, we deploy a federated learning (FL) framework where the labeled data are not shared with the centralized server as well as the peer clients. Secondly, we apply differential privacy (DP) while the models are being trained in each client instance. While both privacy measures are suitable for privacy-aware models, their combination results in unstable models. To our knowledge, this is the first study of its kind on privacy-aware sequence tagging models.
{"title":"An Investigation towards Differentially Private Sequence Tagging in a Federated Framework","authors":"Abhik Jana, Chris Biemann","doi":"10.18653/V1/2021.PRIVATENLP-1.4","DOIUrl":"https://doi.org/10.18653/V1/2021.PRIVATENLP-1.4","url":null,"abstract":"To build machine learning-based applications for sensitive domains like medical, legal, etc. where the digitized text contains private information, anonymization of text is required for preserving privacy. Sequence tagging, e.g. as done in Named Entity Recognition (NER) can help to detect private information. However, to train sequence tagging models, a sufficient amount of labeled data are required but for privacy-sensitive domains, such labeled data also can not be shared directly. In this paper, we investigate the applicability of a privacy-preserving framework for sequence tagging tasks, specifically NER. Hence, we analyze a framework for the NER task, which incorporates two levels of privacy protection. Firstly, we deploy a federated learning (FL) framework where the labeled data are not shared with the centralized server as well as the peer clients. Secondly, we apply differential privacy (DP) while the models are being trained in each client instance. While both privacy measures are suitable for privacy-aware models, their combination results in unstable models. To our knowledge, this is the first study of its kind on privacy-aware sequence tagging models.","PeriodicalId":270632,"journal":{"name":"Proceedings of the Third Workshop on Privacy in Natural Language Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115796609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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