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 122 for “"Federated Learning"”.
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Towards Intelligent Federated Learning Systems
… the privacy challenges of traditional machine learning have become more visible. Recent works leverage edge computing to preserve data privacy by keeping the data where it is (not shared during the training process), so-called "Edge Computing". In 2016, Google extended this idea to distributed …
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FEDERATED LEARNING OF BAYESIAN NEURAL NETWORKS
Although federated learning and Bayesian neural networks have been researched, there are few implementations of the federated learning of Bayesian networks. In this thesis, a federated learning training environment for Bayesian neural networks using a public code base, Flower, is developed. With it …
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Federated Learning for Secure Sensor Cloud
… to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a more reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure …
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Robustness and Reliability of Federated Learning
Federated Learning (FL) is a newly introduced distributed learning scheme, which is designed with users' privacy in mind, by never collecting clients' data during the training process. FL's process starts with the server sending a model to clients, then the clients train that model using their …
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Energy and time efficient federated learning
… as the number of edge devices surges. Federated learning (FL) enables on-device training while preserving privacy, but edge devices typically operate under tight time and energy budgets, highlighting the need for time- and energy-efficient FL algorithms. While prior work focuses on …
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Federated learning in drone-based systems
… who are hesitant to share fine granular data). Federated learning is a natural choice in applications where drones do not want to share their data with any other entity. The federated learning framework comprises several clients (drones) and a server (a base station), where each drone generates …
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Secure and scalable robust federated learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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A Practical Approach to Federated Learning
Machine 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 …
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Federated Learning for Resource Constrained Devices
… insights from multiple devices at the same time. Federated learning allows us to learn from multiple devices in a decentralized manner without requiring data to be shared. Each client trains its own model and communicates relevant model information to a central server. The server aggregates this …
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Federated Learning With Generalization To New Domains
Federated Learning (FL) is an area of research that focuses on training machine learning models in a decentralized 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 …
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Distributed and Federated Learning over IoT networks
L'abstract è presente nell'allegato / the abstract is in the attachment
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Studies in Differential Privacy and Federated Learning
In the late 20th century, Machine Learning underwent a paradigm shift from model-driven to data-driven design. Rather than field specific models, advances in sensors, data storage, and computing power enabled the collection of increasing amounts of data. The abundance of new data allowed …
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On Principles of Efficiency for Federated Learning
… edge computing technologies. In this landscape, Federated Learning (FL) has emerged as a crucial machine learning paradigm that enables collaborative model training across various institutions or devices, crucially preserving the privacy of raw and sensitive data. This approach is particularly …
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Breaking Privacy in Model-Heterogeneous Federated Learning
Federated learning (FL) is a communication protocol that allows multiple distrustful clients to collaboratively train a machine learning model. In FL, data never leaves client devices; instead, clients only share locally computed gradients or model parameters with a central server. As individual …
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Hybrid Distributed Stochastic Gradient Descent for Federated Learning
… sets up a perfect playground for deep learning, which is able to utilize the large volumes of data to achieve various tasks. However, as both the volumes of data and the complexity of neural network architecture rises, it becomes increasingly expensive to train the model on a single …
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Testing Federated Learning Privacy Through Gradient Leakage Attacks
Federated Learning (FL) has recently gained popularity as a way of collaboratively training machine learning models across multiple clients. FL training proceeds in communication rounds, in which each client receives a global model from the FL server and sends back parameter updates computed on its …
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Towards federated learning over large-scale streaming data
… We have designed and developed ORCA, a federated learning architecture that supports the training of traditional Artificial Neural Networks as well as Convolutional Neural Networks and Long Short-term Memory Network based models while ensuring resiliency during scaling. ORCA also …
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Multi-server federated learning in vehicular edge computing
Federated learning (FL) offers a promising paradigm for privacy-preserving model training in connected and autonomous vehicle networks, where vehicles act as clients and roadside units (RSUs) host FL servers at the edge. However, practical deployments face multiple challenges: highly non-IID and …
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