Federated Learning (FL) is rapidly gaining traction as a method for decentralized machine learning, enabling multiple parties to train machine learning models without sharing their data. However, alongside this potential, challenges arise. One such challenge is the threat posed by… Continue Reading →
In the ever-evolving realm of machine learning, federated learning has emerged as a game-changer, especially in scenarios where data privacy is paramount. As technology advances, the demand for decentralized machine learning strategies that accommodate the complexities of non-IID data is… Continue Reading →
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