The field of federated learning is moving towards enhancing security and privacy, with a focus on developing robust frameworks that can resist data poisoning attacks and maintain data confidentiality. Recent developments have introduced innovative methods, such as prototype-based learning and Weibull distribution-based defense mechanisms, which have shown promising results in improving model performance and security. These advancements have the potential to significantly impact the deployment of federated learning in real-world applications, particularly in scenarios where data privacy is a major concern. Noteworthy papers include PPFPL, which proposes a privacy-preserving federated prototype learning framework, and WeiDetect, which introduces a two-phase server-side defense mechanism for detecting malicious participants. FedFeat+ is also notable for its robust federated learning framework that separates feature extraction from classification and incorporates differential privacy mechanisms.