Federated Learning for Early Dropout Prediction on Healthy Ageing Applications

• URL: http://arxiv.org/abs/2309.04311v1
• Date: Fri, 8 Sep 2023 13:17:06 GMT
• Title: Federated Learning for Early Dropout Prediction on Healthy Ageing Applications
• Authors: Christos Chrysanthos Nikolaidis, Vasileios Perifanis, Nikolaos Pavlidis, Pavlos S. Efraimidis
• Abstract summary: We present a federated machine learning (FML) approach that minimizes privacy concerns and enables distributed training, without transferring individual data. Our results show that data selection and class imbalance handling techniques significantly improve the predictive accuracy of models trained under FML.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The provision of social care applications is crucial for elderly people to improve their quality of life and enables operators to provide early interventions. Accurate predictions of user dropouts in healthy ageing applications are essential since they are directly related to individual health statuses. Machine Learning (ML) algorithms have enabled highly accurate predictions, outperforming traditional statistical methods that struggle to cope with individual patterns. However, ML requires a substantial amount of data for training, which is challenging due to the presence of personal identifiable information (PII) and the fragmentation posed by regulations. In this paper, we present a federated machine learning (FML) approach that minimizes privacy concerns and enables distributed training, without transferring individual data. We employ collaborative training by considering individuals and organizations under FML, which models both cross-device and cross-silo learning scenarios. Our approach is evaluated on a real-world dataset with non-independent and identically distributed (non-iid) data among clients, class imbalance and label ambiguity. Our results show that data selection and class imbalance handling techniques significantly improve the predictive accuracy of models trained under FML, demonstrating comparable or superior predictive performance than traditional ML models.

Related papers

• Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data [10.626503137418636]
  Self-supervised learning (SSL) has become the de facto training paradigm of large models. This study explores the impact of pre-training and fine-tuning strategies on fairness.
  arXiv  Detail & Related papers  (2024-01-03T09:31:43Z)
• Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models [39.42802115580677]
  Semi-supervised learning (SSL) can leverage both labeled and unlabeled data to build a predictive model. Recent literature suggests that naively applying state-of-the-art SSL with a pretrained model fails to unleash the full potential of training data. We propose to use pseudo-labels from the unlabelled data to update the feature extractor that is less sensitive to incorrect labels.
  arXiv  Detail & Related papers  (2023-09-09T01:57:14Z)
• Non-Invasive Fairness in Learning through the Lens of Data Drift [88.37640805363317]
  We show how to improve the fairness of Machine Learning models without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift. We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data.
  arXiv  Detail & Related papers  (2023-03-30T17:30:42Z)
• A Survey on Class Imbalance in Federated Learning [6.632451878730774]
  Federated learning allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data. It has been found that models trained with federated learning usually have worse performance than their counterparts trained in the standard centralized learning mode.
  arXiv  Detail & Related papers  (2023-03-21T08:34:23Z)
• Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data [89.79617468457393]
  Training models with imbalance rate (class density discrepancy) may lead to suboptimal prediction. We propose a framework for training models for this imbalance issue. We demonstrate our model's improved performance in real-world medical datasets.
  arXiv  Detail & Related papers  (2022-07-23T00:39:53Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)
• Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging [23.08596805950814]
  We present a robust and label-efficient self-supervised FL framework for medical image analysis. Specifically, we introduce a novel distributed self-supervised pre-training paradigm into the existing FL pipeline. We show that our self-supervised FL algorithm generalizes well to out-of-distribution data and learns federated models more effectively in limited label scenarios.
  arXiv  Detail & Related papers  (2022-05-17T18:33:43Z)
• FORML: Learning to Reweight Data for Fairness [2.105564340986074]
  We introduce Fairness Optimized Reweighting via Meta-Learning (FORML) FORML balances fairness constraints and accuracy by jointly optimizing training sample weights and a neural network's parameters. We show that FORML improves equality of opportunity fairness criteria over existing state-of-the-art reweighting methods by approximately 1% on image classification tasks and by approximately 5% on a face prediction task.
  arXiv  Detail & Related papers  (2022-02-03T17:36:07Z)
• Learning from Heterogeneous Data Based on Social Interactions over Graphs [58.34060409467834]
  This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting. We show that the. strategy enables the agents to learn consistently under this highly-heterogeneous setting.
  arXiv  Detail & Related papers  (2021-12-17T12:47:18Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

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.