Inverse Distance Aggregation for Federated Learning with Non-IID Data

Yeganeh, Yousef; Farshad, Azade; Navab, Nassir; Albarqouni, Shadi

doi:10.1007/978-3-030-60548-3_15

Cited by 72 publications

(35 citation statements)

References 30 publications

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“…Federated learning models, such as decentralized independent component analysis ( Baker et al, 2015 ), sparse regression ( Plis et al, 2016 ), and distributed deep learning ( Kaissis et al, 2021 ; Stripelis et al, 2021 ; Warnat-Herresthal et al, 2021 ), have made solid progress with leveraging multivariate image features for statistical inferences, allowing iterative computation on remote datasets. Some other recent studies focus on multivariate linear modeling ( Silva et al, 2020 ), federated gradient averaging ( Remedios et al, 2020 ), and unbalanced data for multi-site ( Yeganeh et al, 2020 ). To our knowledge, these methods have not yet been applied to detect multimodal associations in AD research, such as finding anatomically abnormal regions on MRI that are associated with Aβ/tau pathology defined using PET.…”

Section: Introductionmentioning

confidence: 99%

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Dong

Zhang

et al. 2021

Front. Neurosci.

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Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.

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Section: Introductionmentioning

confidence: 99%

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Dong

Zhang

et al. 2021

Front. Neurosci.

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“…Similar to the work presented in this paper, other schemes can be designed to prioritize the (source) samples via a data scheduler, for instance, motivated by boosting [52]. Regarding the local model aggregation, one could deploy a CL-based adaptive weighting for clients based on a dynamic scoring function taking into account meta-information [21], and in this way, help to cope with unbalanced and non-IID data. Finally, to improve alignment, scoring functions could rely on computing the distance between (noisy) latent representations of the source and the remaining domains to weigh each local model contribution.…”

Section: Discussionmentioning

confidence: 99%

“…Only few FL works have been shown effective on medical images. For instance, for brain tumor segmentation [14]- [16]; for prediction of disease incidence, patient response to treatment, and other healthcare events [17]; and lately for classification [8], [18]- [21]. Regarding breast imaging, only Roth et al [10] have investigated breast density classification.…”

Section: Related Work a Federated Learningmentioning

confidence: 99%

Memory-aware curriculum federated learning for breast cancer classification

Jiménez-Sánchez¹,

Tardy²,

Ballester³

et al. 2021

Preprint

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For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative samples. A potential solution to such class-imbalance is joining forces across multiple institutions. Developing a collaborative computer-aided diagnosis system is challenging in different ways. Patient privacy and regulations need to be carefully respected. Data across institutions may be acquired from different devices or imaging protocols, leading to heterogeneous non-IID data. Also, for learning-based methods, new optimization strategies working on distributed data are required. Recently, federated learning has emerged as an effective tool for collaborative learning. In this setting, local models perform computation on their private data to update the global model. The order and the frequency of local updates influence the final global model. Hence, the order in which samples are locally presented to the optimizers plays an important role. In this work, we define a memoryaware curriculum learning method for the federated setting.Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model. Our approach is combined with unsupervised domain adaptation to deal with domain shift while preserving data privacy. We evaluate our method with three clinical datasets from different vendors. Our results verify the effectiveness of federated adversarial learning for the multi-site breast cancer classification. Moreover, we show that our proposed memoryaware curriculum method is beneficial to further improve classification performance. Our code is publicly available at: https://github.com/ameliajimenez/curriculum-federatedlearning.

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“…Federated learning models, such as decentralized independent component analysis (Baker et al, 2015), sparse regression (Plis et al, 2016), and distributed deep learning (Kaissis et al, 2021;Stripelis et al, 2021;Warnat-Herresthal et al, 2021), have made solid progress with leveraging multivariate image features for statistical inferences, allowing iterative computation on remote datasets. Some other recent studies focus on multivariate linear modeling (Silva et al, 2020), federated gradient averaging (Remedios et al, 2020), and unbalanced data for multi-site (Yeganeh et al, 2020). To our knowledge, these methods have not yet been applied to detect multimodal associations in Alzheimer's disease research, such as finding anatomically abnormal regions on MRI that are associated with Aβ/tau pathology defined using PET.…”

Section: Introductionmentioning

confidence: 99%

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Dong

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two ADNI cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI) and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89x speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.

show abstract

Inverse Distance Aggregation for Federated Learning with Non-IID Data

Cited by 72 publications

References 30 publications

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Memory-aware curriculum federated learning for breast cancer classification

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

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