## 2 Background
### Communication
These algorithms facilitate the development of personalized models for users while ensuring the decentralization of their sensitive data. Personalization algorithms utilize data from local interactions and activities on user devices to discern trends and preferences. FL enables the integration of these insights into the global model while upholding user privacy. This practice ensures that recommendations, services, or materials provided to consumers are highly relevant and engaging, aligning with their preferences and needs [42]. FL empowers organizations and service providers to deliver personalized experiences on a large scale, simultaneously enhancing user satisfaction and safeguarding their privacy and data security. Personalization algorithms can be applied to tailor both global and local models within FLSs. * Global model personalization in Federated Learning Systems: Global model personalization within Federated Learning Systems (FLSs) is a sophisticated approach that aims to adapt a universally trained model to meet the specific needs and preferences of individual users or user groups. This concept is particularly vital in ensuring that the one-size-fits-all model can be effectively tailored to diverse user contexts while preserving privacy and data security. Global model personalization involves the adaptation of a shared global model, initially trained across multiple devices or data sources, to better align with the unique characteristics, behaviors, or preferences of individual users or specific segments [43]. This adaptation is crucial in FLSs, where a single global model is collaboratively trained but needs to be relevant and effective for each participant in the system. Techniques for global model personalization:
* Client-side personalization: This involves adjusting the global model on the client's device using local data. Techniques such as model fine-tuning, where the model is slightly adjusted using the user's data, or layer retraining, where specific layers of the model are retrained, are commonly used. * User embeddings: Incorporating user embeddings into the model is another effective method. User embeddings are vector representations that capture the unique characteristics of each user. These embeddings can be integrated into the global model to ensure that the model's outputs are personalized for each user. * Transfer learning: Leveraging transfer learning, where a model trained on one task is adapted for another related task, can also be employed for personalization. This is particularly useful when the global model is trained on a broad dataset but needs to be adapted for specific user scenarios. Meta-learning: Meta-learning, or learning to learn, is a technique where the model is trained to quickly adapt to new tasks or data. In the context of personalization, meta-learning can enable the global model to rapidly adjust to individual user data. Challenges in global model personalization:  
* Data diversity and quality: Ensuring that the global model can effectively personalize across a wide range of diverse user data is a significant challenge. * Resource limitations: The computational and storage limitations of client devices must be considered, especially when personalization involves additional model training on the device. * Privacy concerns: Maintaining user privacy during the personalization process, especially when user-specific data are used for model adjustments, is crucial. Global model personalization in FLSs represents a key strategy in making Federated Learning models more user-centric and effective. By adapting the shared global model to align with individual users' unique tastes and features, FLSs can provide customized and relevant experiences to users, enhancing the overall utility and acceptance of these systems. * Local model personalization in Federated Learning Systems: Local model personalization in Federated Learning Systems (FLSs) addresses the challenge of customizing machine learning models at an individual level, using data that reside on a user's device.