The Current State and Challenges of Fairness in Federated Learning

Vucinich, Sean; Zhu, Qiang

doi:10.1109/access.2023.3295412

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…While federated learning offers distributed training, it's not fully decentralized. It relies on a central server to aggregate locally trained models, posing various data security concerns 11 . Blockchain has emerged as an alternative solution to third-party servers due to its fully decentralized nature [12][13][14] [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Fast-CFLB: A Privacy-Preserving Data Sharing System for Internet of Vehicles Using Ternary Federated Learning and Blockchain

Li,

2024

Preprint

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Vehicular networking technology using federated learning enhances data privacy and security compared to centralized methods. Yet, it requires further refinement to combat single-point failure and membership inference attacks, privacy concerns, and communication expenses. This paper employs federated differential privacy and blockchain integration to address these challenges, alongside ternary gradient technology and model compression to reduce communication costs. In differential privacy experiments, we have determined that federated differential privacy protection is closer to the accuracy of a no-privacy protection scheme compared to traditional differential privacy protection, especially when C ≥ 2. In ternary gradient experiments, we observed a reduction in training gradients of 14.99×, 15.54×, and 15.97× across three datasets. In layer sensitivity experiments, we found that the accuracy at top = 97%, 94%, and 91% is comparable to that at top = 100% (uncompressed). In blockchain experiments, The training effects and overall trends of blockchain models are similar to those of non-blockchain models.

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Section: Literature Reviewmentioning

confidence: 99%

Fast-CFLB: A Privacy-Preserving Data Sharing System for Internet of Vehicles Using Ternary Federated Learning and Blockchain

Li,

2024

Preprint

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“…Therefore, it is vital to investigate avenues that can amalgamate these two latest technologies and improve the technical aspects (or QoS) of the FL paradigm. Lastly, FL may yield biased results in the training process, owing to the higher disparities in data among participating clients [80]. Hence, it is imperative to develop practical methods to ensure fairness and to prevent discrimination against any client during the training.…”

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confidence: 99%

Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case

Hwang,

Majeed

2024

Applied Sciences

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Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms that can effectively work with decentralized data sources (e.g., hospitals) without acquiring any private data. Recently, applications of FL have vastly expanded into multiple domains, particularly the medical domain, and FL is becoming one of the mainstream technologies of the near future. In this study, we provide insights into FL fundamental concepts (e.g., the difference from centralized learning, functions of clients and servers, workflows, and nature of data), architecture and applications in the general medical domain, synergies with emerging technologies, key challenges (medical domain), and potential research prospects. We discuss major taxonomies of the FL systems and enlist technical factors in the FL ecosystem that are the foundation of many adversarial attacks on these systems. We also highlight the promising applications of FL in the medical domain by taking the recent COVID-19 pandemic as an application use case. We highlight potential research and development trajectories to further enhance the persuasiveness of this emerging paradigm from the technical point of view. We aim to concisely present the progress of FL up to the present in the medical domain including COVID-19 and to suggest future research trajectories in this area.

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A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities

Saha,

Hota,

Chattopadhyay

et al. 2024

Artif Intell Rev

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Federated learning (FL) refers to a system of training and stabilizing local machine learning models at the global level by aggregating the learning gradients of the models. It reduces the concern of sharing the private data of participating entities for statistical analysis to be carried out at the server. It allows participating entities called clients or users to infer useful information from their raw data. As a consequence, the need to share their confidential information with any other entity or the central entity called server is eliminated. FL can be clearly interpreted as a privacy-preserving version of traditional machine learning and deep learning algorithms. However, despite this being an efficient distributed training scheme, the client’s sensitive information can still be exposed to various security threats from the shared parameters. Since data has always been a major priority for any user or organization, this article is primarily concerned with discussing the significant problems and issues relevant to the preservation of data privacy and the viability and feasibility of several proposed solutions in the FL context. In this work, we conduct a detailed study on FL, the categorization of FL, the challenges of FL, and various attacks that can be executed to disclose the users’ sensitive data used during learning. In this survey, we review and compare different privacy solutions for FL to prevent data leakage and discuss secret sharing (SS)-based security solutions for FL proposed by various researchers in concise form. We also briefly discuss quantum federated learning (QFL) and privacy-preservation techniques in QFL. In addition to these, a comparison and contrast of several survey works on FL is included in this work. We highlight the major applications based on FL. We discuss certain future directions pertaining to the open issues in the field of FL and finally conclude our work.

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The Current State and Challenges of Fairness in Federated Learning

Cited by 5 publications

References 40 publications

Fast-CFLB: A Privacy-Preserving Data Sharing System for Internet of Vehicles Using Ternary Federated Learning and Blockchain

Fast-CFLB: A Privacy-Preserving Data Sharing System for Internet of Vehicles Using Ternary Federated Learning and Blockchain

Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case

A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities

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