Adaptive Federated Learning Architecture To Mitigate Non-IID Through Multi-Objective GA-Based Efficient Client Selection
Federated Learning (FL) has emerged as a promising framework for collaborative model training across distributed devices without centralizing sensitive data. However, FL encounters significant challenges when dealing with non-independent and non-identically distributed (Non-IID) data across particip...
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| Format: | masterThesis |
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2024
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| Online Access: | http://hdl.handle.net/10725/16528 https://doi.org/10.26756/th.2023.748 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
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