A Multivocal Literature Review on Privacy and Fairness in Federated Learning

• URL: http://arxiv.org/abs/2408.08666v2
• Date: Sun, 27 Oct 2024 11:08:39 GMT
• Title: A Multivocal Literature Review on Privacy and Fairness in Federated Learning
• Authors: Beatrice Balbierer, Lukas Heinlein, Domenique Zipperling, Niklas Kühl,
• Abstract summary: Federated learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Recent research has demonstrated an inherent tension between privacy and fairness. We argue that the relationship between privacy and fairness has been neglected, posing a critical risk for real-world applications.
• Score: 1.6124402884077915
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated Learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Yet, research has shown that information can still be extracted during training, making additional privacy-preserving measures such as differential privacy imperative. To implement real-world federated learning applications, fairness, ranging from a fair distribution of performance to non-discriminative behaviour, must be considered. Particularly in high-risk applications (e.g. healthcare), avoiding the repetition of past discriminatory errors is paramount. As recent research has demonstrated an inherent tension between privacy and fairness, we conduct a multivocal literature review to examine the current methods to integrate privacy and fairness in federated learning. Our analyses illustrate that the relationship between privacy and fairness has been neglected, posing a critical risk for real-world applications. We highlight the need to explore the relationship between privacy, fairness, and performance, advocating for the creation of integrated federated learning frameworks.

Related papers

• DEAN: Deactivating the Coupled Neurons to Mitigate Fairness-Privacy Conflicts in Large Language Models [34.73299042341535]
  We introduce a training-free method to textbfDEActivate the fairness and privacy coupled textbfNeurons (textbfDEAN), which theoretically and empirically decrease the mutual information between fairness and privacy awareness. Extensive experimental results demonstrate that DEAN eliminates the trade-off phenomenon and significantly improves LLMs' fairness and privacy awareness simultaneously.
  arXiv  Detail & Related papers  (2024-10-22T04:08:27Z)
• Towards Split Learning-based Privacy-Preserving Record Linkage [49.1574468325115]
  Split Learning has been introduced to facilitate applications where user data privacy is a requirement. In this paper, we investigate the potentials of Split Learning for Privacy-Preserving Record Matching.
  arXiv  Detail & Related papers  (2024-09-02T09:17:05Z)
• Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives [48.48294460952039]
  This survey offers comprehensive descriptions of the privacy, security, and fairness issues in federated learning. We contend that there exists a trade-off between privacy and fairness and between security and sharing.
  arXiv  Detail & Related papers  (2024-06-16T10:31:45Z)
• Differentially Private Federated Learning: A Systematic Review [35.13641504685795]
  We propose a new taxonomy of differentially private federated learning based on definition and guarantee of various differential privacy models and scenarios. Our work provide valuable insights into privacy-preserving federated learning and suggest practical directions for future research.
  arXiv  Detail & Related papers  (2024-05-14T03:49:14Z)
• Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy [26.307780067808565]
  This study introduces a theoretical framework that enables a comprehensive examination of the interplay between privacy and fairness. We shall develop and analyze an information bottleneck (IB) based information obfuscation method with local differential privacy (LDP) for fair representation learning. In contrast to many empirical studies on fairness in ML, we show that the incorporation of LDP randomizers during the encoding process can enhance the fairness of the learned representation.
  arXiv  Detail & Related papers  (2024-02-16T06:35:10Z)
• Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory [82.7042006247124]
  We show that even the most capable AI models reveal private information in contexts that humans would not, 39% and 57% of the time, respectively. Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.
  arXiv  Detail & Related papers  (2023-10-27T04:15:30Z)
• Holistic Survey of Privacy and Fairness in Machine Learning [10.399352534861292]
  Privacy and fairness are crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML) Despite significant interest, there remains an immediate demand for more in-depth research to unravel how these two objectives can be simultaneously integrated into ML models. We provide a thorough review of privacy and fairness in ML, including supervised, unsupervised, semi-supervised, and reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-28T23:39:29Z)
• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [58.107474025048866]
  Forgetting refers to the loss or deterioration of previously acquired knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
  arXiv  Detail & Related papers  (2023-07-16T16:27:58Z)
• Privacy and Fairness in Federated Learning: on the Perspective of Trade-off [58.204074436129716]
  Federated learning (FL) has been a hot topic in recent years. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied.
  arXiv  Detail & Related papers  (2023-06-25T04:38:19Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)
• Differentially private cross-silo federated learning [16.38610531397378]
  Strict privacy is of paramount importance in distributed machine learning. In this paper we combine additively homomorphic secure summation protocols with differential privacy in the so-called cross-silo federated learning setting. We demonstrate that our proposed solutions give prediction accuracy that is comparable to the non-distributed setting.
  arXiv  Detail & Related papers  (2020-07-10T18:15:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.