Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 36 for “"Data heterogeneity"”.
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Effects of Data Heterogeneity on Distributed Linear System Solvers
We focus on the fundamental problem of solving a system of linear equations. In particular, we are interested in distributed linear system solvers, where one taskmaster coordinates any number of workers to attain a solution. There are two predominant and fundamentally different ways of doing this: …
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Analysis of XML and COIN as solutions for data heterogeneity in insurance system integration
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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Contributions for Handling Big Data Heterogeneity. Using Intuitionistic Fuzzy Set Theory and Similarity Measures for Classifying Heterogeneous Data
A huge amount of data is generated daily by digital technologies such as social media, web logs, traffic sensors, on-line transactions, tracking data, videos, and so on. This has led to the archiving and storage of larger and larger datasets, many of which are multi-modal, or contain different …
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Federated Learning With Generalization To New Domains
… fashion without having the need to store all data on one central server. In this thesis, we address the challenges of data heterogeneity and label scarcity in FL by proposing two novel approaches for federated domain generalization in both unsupervised and supervised settings. First, to tackle …
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Globalization for Scalable Short-term Forecasting of Heterogeneous Loads
… expensive and less efficient as network size and data volume grow. In contrast, global forecasting models (GFMs) enhance generalizability, scalability, and robustness through globalization and cross-learning. However, GFMs assume that input time series are inherently related, overlooking potential …
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Communication-efficient and privacy-preserving statistical learning with applications in high dimensional data
… machine learning. Instead of aggregating raw data--which creates significant privacy vulnerabilities and complicates compliance with regulations like GDPR and HIPAA--federated learning brings the model to the data, enabling collaborative training while data remains permanently decentralized. …
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Bayesian Modeling of Complex High-Dimensional Data
… can now collect high-dimensional complex data in different forms, such as medical images, genomics measurements. However, acquisition of more data does not automatically lead to better knowledge discovery. One needs efficient and reliable analytical tools to extract useful information from …
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A Systems Approach to Modelling Tumour and Tissue Response in Radiotherapy for Head-and-Neck Cancer
The heterogeneity of patient responses to radiotherapy poses significant challenges in head-and-neck cancer (HNC), with outcomes ranging from severe toxicities to loco-regional recurrence (LRR). Multi-modal biomarker data—encompassing genetic, dosimetric, and imaging domains—offers an opportunity …
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A Practical Approach to Federated Learning
… models benefit from large and diverse training datasets. However, it is difficult for an individual organization to collect sufficiently diverse data. Additionally, the sensitivity of the data and government regulations such as GDPR, HIPPA, and CCPA restrict how organizations can share data with …
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TRUST: Clinical Text Retrieval and Use towards Scientific Rigor and Transparent Process
… of first deriving knowledge from routine care data and then translating such knowledge into evidence-based clinical practice. To achieve such a vision, it is critical to have a robust data and informatics infrastructure with the following properties: 1) high-throughput and real-time methods for …
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Privacy-aware Federated Learning with Global Differential Privacy
… federated learning systems is also affected by data heterogeneity. Federated learning systems are capable of protecting the private data of clients from adversaries. However, by analyzing the uploaded client parameters, confidential information can still be revealed. To combat privacy attacks on …
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Towards a Reliable Deep Learning Framework for Prostate Cancer Diagnosis using Ultrasound
… detection is hindered by noisy labels and cancer heterogeneity. The purpose of this work is to develop a clinically applicable framework for DL-based detection of PCa from ultrasound that is robust to noise and uncertainty inherent to ultrasound images and their associated gold standard pathology …
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Scaffold Perception, ComPharmacophore Model Development, And Quantitative Structure-Affinity Relationships Of Sigma Site Ligands
… utility, and confronted by the obstacles of data heterogeneity, a database of σ ligands and their binding affinity data was collected. Cohorts of data collected under similar experimental methodologies were assembled and clustered by measures of scaffold dissimilarity. Multiple-Instance …
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Computational Analysis of LC-MS/MS Data for Metabolite Identification
… putative identifications of one peak in LC-MS data. Through the framework, peaks are likely to have the m/z values that can give appropriate putative identifications. And important guidance for the metabolite verification is provided by prioritizing the putative identifications. Third, an MS/MS …
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Effective Modeling in Medical Imaging with Constrained Data
Data for modern medical imaging modeling is constrained by their high physical density, complex structure, insufficient annotation, heterogeneity across sites, long-tailed distribution of findings/conditions/diseases, and sparsely presented information. In this dissertation, to utilize the …
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On information organization and information extraction for the study of gene expressions by tissue microarray technique
… is dramatically expanding the amount of data available on many disease states. These studies typically involve many researchers with different backgrounds, each contributing to some steps of the entire process. In particular, Tissue Microarray technology allows for high-throughput …
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REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
… enforces privacy by allowing the user's local data to reside on their device. Instead of having users send their personal data to a server where the model resides, FL flips the paradigm and brings the model to the user's device for training. Existing works share model parameters or use …
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The role of semantic web technologies for IoT data in underpinning environmental science
… to generate a huge amount of heterogeneous data at different geographical locations and with various temporal resolutions in environmental science. In many other areas of IoT deployment, volume and velocity dominate, however in environmental science, the more general pattern is quite …
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Decentralized Machine Learning over Fragmented Data
The remarkable scaling of data and computation has unlocked unprecedented capabilities in text and image generation, raising the question: Why hasn’t healthcare seen similar breakthroughs? This disparity stems primarily from healthcare data being fragmented across thousands of institutions, each …
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Towards Secure and Resilient Machine Learning Systems
… a decentralized learning paradigm that preserves data privacy while enabling collaborative model training across distributed clients. Given its advantages in privacy-sensitive domains, FL provides a compelling foundation for cybersecurity applications, particularly for enhancing Intrusion …
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