ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2021
Federated Adversarial Debiasing for Fair and Transferable Representations.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring gender biases in ML and AI academic research through systematic literature review.Frontiers in artificial intelligence · 2022Pooled it
- AI models, bias and data sharing efforts to tackle Alzheimer's disease and related dementias.The journal of prevention of Alzheimer's disease · 2025Article
- Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.Patterns (New York, N.Y.) · 2024Review
- Unified fair federated learning for digital healthcare.Patterns (New York, N.Y.) · 2024Article
- Algorithmic fairness in artificial intelligence for medicine and healthcare.Nature biomedical engineering · 2023Review
- Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling.Advances in neural information processing systems · 2022Article
- Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.Proceedings of machine learning research · 2022Article
- Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization.... International Conference on Learning Representations · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Federated learning is a distributed learning framework that is communication efficient and provides protection over participating users' raw training data. One outstanding challenge of federate learning comes from the users' heterogeneity, and learning from such data may yield biased and unfair models for minority groups. While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework. In this work, we study these barriers and address them by proposing a novel approach Federated Adversarial DEbiasing (FADE). FADE does not require users' sensitive group information for debiasing and offers users the freedom to opt-out from the adversarial component when privacy or computational costs become a concern. We show that ideally, FADE can attain the same global optimality as the one by the centralized algorithm. We then analyze when its convergence may fail in practice and propose a simple yet effective method to address the problem. Finally, we demonstrate the effectiveness of the proposed framework through extensive empirical studies, including the problem settings of unsupervised domain adaptation and fair learning. Our codes and pre-trained models are available at: https://github.com/illidanlab/FADE.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.