Fortifying federated learning: security against model poisoning attacks

Fortifying federated learning: security against model poisoning attacks

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Xehetasun bibliografikoak
Egile Nagusiak:	Anan, Fabiha, Mamun, Kazi Shahed, Kamal, Md Sifat, Ahsan, Nizbath
Beste egile batzuk:	Hossain, Muhammad Iqbal
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2024
Gaiak:	Model poisoning Federated learning Machine learning Deep learning Computer networks > Security measures
Sarrera elektronikoa:	http://hdl.handle.net/10361/22774

Antzeko izenburuak

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Application of CNN based architectures in detection of distracted drivers
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EAI4CC: deciphering lung and colon cancer categorization within a federated learning framework harnessing the power of explainable artificial intelligence
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Pothole detection using lightweight network models
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Facial expression recognition: convolutional attentional masking network and ensemble approach
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Implementation of real-time learning on homomorphically encrypted visual inputs
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Analyzing the security of e-Health data based on a hybrid federated learning model
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Predicting the outcome of purchasing a hotel package to assist policymakers using machine learning
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DeepWPD: A deep learning based wavelet packet decomposition to remove Moiré pattern from screen capture images.
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Prostate cancer detection using deep learning neural network with transfer learning approach
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Bang-lish sentiment classification using deep learning
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Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
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Voice recognition using machine learning and central database to enhance security system
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