A Trust and Data Quality-Based Dynamic Node Selection and Aggregation Optimization in Federated Learning

Federated learning (FL) is a cutting-edge approach to machine learning where multiple clients (or nodes) collaboratively train a model while keeping their data localized. This method addresses significant privacy concerns and reduces data centralization risks. However, a key challenge in FL is efficiently selecting which clients contribute to the model and determining how often their updates should be aggregated. This process is crucial for enhancing model performance and maintaining data integrity. This paper introduces the Trust-Based Dynamic Node Selection and Aggregation Frequency Optimization methodology to tackle this challenge using a Deep Q-Network (DQN). We focus on dynamically selecting clients based on a trust metric that evaluates their reliability and the quality of their data contributions. This metric incorporates factors like historical accuracy, frequency of successful contributions, and consistency in participation. Furthermore, we optimize the frequency of aggregating client updates to improve learning efficiency and model accuracy. By integrating these elements, our approach aims to maximize the effectiveness of federated learning, ensuring that reliable and relevant data significantly influences the model, thereby enhancing its overall performance and trustworthiness.