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This poster presents a novel privacy-preserving federated learning algorithm, called Privacy-Preserving Asynchronous Federated Learning (PPAFL), tailored for personalized healthcare applications. The algorithm integrates machine learning advancements and computer networking techniques to address data privacy and communication overhead challenges. Real-world health datasets are used for evaluating the algorithm's effectiveness and scalability. The results show that PPAFL has significant implications for personalized healthcare, bridging machine learning and computer networking to enable effective collaboration while preserving data privacy. This research has the potential to revolutionize data-driven decision-making in healthcare, leading to improved patient outcomes and quality of care. Future work includes the development of advanced privacy-preserving techniques, communication-efficient algorithms, and adaptive learning strategies to further enhance the algorithm's capabilities and generalizability.