Federated Deep AUC Maximization for Heterogeneous Data with a Constant
Communication Complexity
- URL: http://arxiv.org/abs/2102.04635v1
- Date: Tue, 9 Feb 2021 04:05:19 GMT
- Title: Federated Deep AUC Maximization for Heterogeneous Data with a Constant
Communication Complexity
- Authors: Zhuoning Yuan, Zhishuai Guo, Yi Xu, Yiming Ying, Tianbao Yang
- Abstract summary: We propose improved FDAM algorithms for detecting heterogeneous chest data.
A result of this paper is that the communication of the proposed algorithm is strongly independent of the number of machines and also independent of the accuracy level.
Experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets and on medical chest Xray images from different organizations.
- Score: 77.78624443410216
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: \underline{D}eep \underline{A}UC (area under the ROC curve)
\underline{M}aximization (DAM) has attracted much attention recently due to its
great potential for imbalanced data classification. However, the research on
\underline{F}ederated \underline{D}eep \underline{A}UC \underline{M}aximization
(FDAM) is still limited. Compared with standard federated learning (FL)
approaches that focus on decomposable minimization objectives, FDAM is more
complicated due to its minimization objective is non-decomposable over
individual examples. In this paper, we propose improved FDAM algorithms for
heterogeneous data by solving the popular non-convex strongly-concave min-max
formulation of DAM in a distributed fashion. A striking result of this paper is
that the communication complexity of the proposed algorithm is a constant
independent of the number of machines and also independent of the accuracy
level, which improves an existing result by orders of magnitude. Of independent
interest, the proposed algorithm can also be applied to a class of
non-convex-strongly-concave min-max problems. The experiments have demonstrated
the effectiveness of our FDAM algorithm on benchmark datasets, and on medical
chest X-ray images from different organizations. Our experiment shows that the
performance of FDAM using data from multiple hospitals can improve the AUC
score on testing data from a single hospital for detecting life-threatening
diseases based on chest radiographs.
Related papers
- Robust Fiber ODF Estimation Using Deep Constrained Spherical
Deconvolution for Diffusion MRI [7.9283612449524155]
A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF)
measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI.
Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling.
We propose a novel data-driven deep constrained spherical deconvolution method to
arXiv Detail & Related papers (2023-06-05T14:06:40Z) - Angular upsampling in diffusion MRI using contextual HemiHex
sub-sampling in q-space [0.0]
It is important to incorporate relevant context for the data to ensure that maximum prior information is provided for the AI model to infer the posterior.
In this paper, we introduce HemiHex subsampling to suggestively address training data sampling on q-space geometry.
Our proposed approach is a geometrically optimized regression technique which infers the unknown q-space thus addressing the limitations in the earlier studies.
arXiv Detail & Related papers (2022-11-01T03:13:07Z) - A Penalty Approach for Normalizing Feature Distributions to Build
Confounder-Free Models [11.818509522227565]
MetaData Normalization (MDN) estimates the linear relationship between the metadata and each feature based on a non-trainable closed-form solution.
We extend the MDN method by applying a Penalty approach (referred to as PDMN)
We show improvement in model accuracy and greater independence from confounders using PMDN over MDN in a synthetic experiment and a multi-label, multi-site dataset of magnetic resonance images (MRIs)
arXiv Detail & Related papers (2022-07-11T04:02:12Z) - FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine
Transform Loss [58.58979566599889]
We propose a novel self-supervised learning (FedMed) for brain image synthesis.
An affine transform loss (ATL) was formulated to make use of severely distorted images without violating privacy legislation.
The proposed method demonstrates advanced performance in both the quality of synthesized results under a severely misaligned and unpaired data setting.
arXiv Detail & Related papers (2022-01-29T13:45:39Z) - FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality
Brain Image Synthesis [55.939957482776194]
We propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN)
FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators.
A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods.
arXiv Detail & Related papers (2022-01-22T02:50:29Z) - Diffusion Earth Mover's Distance and Distribution Embeddings [61.49248071384122]
Diffusion can be computed in $tildeO(n)$ time and is more accurate than similarly fast algorithms such as tree-baseds.
We show Diffusion is fully differentiable, making it amenable to future uses in gradient-descent frameworks such as deep neural networks.
arXiv Detail & Related papers (2021-02-25T13:18:32Z) - Stochastic Hard Thresholding Algorithms for AUC Maximization [49.00683387735522]
We develop a hard thresholding algorithm for AUC in distributiond classification.
We conduct experiments to show the efficiency and effectiveness of the proposed algorithms.
arXiv Detail & Related papers (2020-11-04T16:49:29Z) - Towards Discriminability and Diversity: Batch Nuclear-norm Maximization
under Label Insufficient Situations [154.51144248210338]
Batch Nuclear-norm Maximization (BNM) is proposed to boost the learning under label insufficient learning scenarios.
BNM outperforms competitors and works well with existing well-known methods.
arXiv Detail & Related papers (2020-03-27T05:04:24Z)
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.