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This section provides insights into the landscape of related works in Federated Learning, focusing on attack detection and defense mechanisms. It critiques existing methodologies, such as k-means clustering for attack detection and various defense strategies for robust learning in FL. The evaluation emphasizes the challenges faced by these approaches, such as reliance on historical data and unintended quality degradation in the absence of attacks.
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