## 2 Background
### Communication
This approach is crucial in FLSs, where maintaining data privacy and catering to specific user needs are paramount. Local model personalization revolves around adapting a federated model to fit individual user profiles based on their unique data. Unlike global model personalization, which modifies a shared model to suit general user characteristics, local personalization focuses on leveraging data available on each user's device to create a model that reflects their specific preferences, behaviors, and usage patterns. Techniques for local model personalization:
* On-device training: This involves adjusting the federated model directly on the user's device. The model is fine-tuned with the user's local data, ensuring that the personalized model captures individual preferences and behaviors. * Data augmentation: Enhancing the local training process with data augmentation techniques can improve the model's ability to learn from a limited amount of user data. This might include generating synthetic data points based on the user's existing data to provide a more comprehensive training dataset. * Layer customization: In some cases, only specific layers of the neural network are personalized, while others remain shared across all users. This approach can be particularly effective in scenarios where certain aspects of the model need to be user-specific, while others can benefit from broader, global training. * User feedback integration: Incorporating user feedback directly into the training process allows the model to adapt dynamically to changing user preferences and behaviors. This can be achieved through techniques like reinforcement learning, where the model learns and adapts based on user interactions. Challenges in local model personalization:
* Resource constraints: Personalizing models on individual devices requires computational and storage resources, which might be limited, especially in mobile or IoT devices. * Data quality and diversity: The quality and diversity of local data can significantly impact the effectiveness of personalization. Ensuring that the model can handle a variety of data types and qualities is essential. * Privacy preservation: Even though the data do not leave the device, ensuring that the personalization process itself does not compromise user privacy is crucial. Advancements in lightweight machine learning models, efficient on-device training algorithms, and privacy-preserving techniques will be key to enhancing local model personalization. Research into optimizing these elements can lead to more effective and user-friendly personalized experiences in FLSs. Local model personalization in FLSs represents a critical step towards creating more user-centric and efficient learning models. By leveraging local data to tailor models to individual user needs, FLSs can provide more relevant, accurate, and privacy-preserving services. This personalized approach not only enhances user experience but also drives the effectiveness and adaptability of learning models in diverse real-world scenarios. * Outlier handling algorithms: Handling outliers is a crucial aspect of data analysis and statistical modeling. Outliers are data points that significantly deviate from the majority [44]. Algorithms within FLSs play a vital role in maintaining the precision and reliability of machine learning models, especially when dealing with noisy or aberrant data points. These methods focus on detecting and managing outliers, which are data examples that deviate substantially from the established norm. The presence of outliers within a dataset can introduce bias during the model training process, potentially compromising the accuracy of subsequent predictions. The management of outliers is of utmost importance in FL, which involves utilizing data from various heterogeneous sources. Once outliers are identified, they can be addressed through data cleaning, imputation, or robust model training techniques.