Navigating Explainable Privacy in Federated Learning

With the dawn of distributed Artificial Intelligence (AI) accelerated with the upcoming Beyond 5G (BSG)/6G networks, Federated Learning (FL) is emerging as an innovative approach to performing distributed learning in a privacy-preserved manner. Numerous techniques are available for fine-tuning AI-based parameters in FL. Depending on factors such as specifications, use cases, and limitations, this tuning method can vary among different FL processes, which is essential to consider when designing and developing FL-based systems. Therefore, in this research, we conduct an investigation on the variation and trade-offs of such AI-based parameters, with metrics including user diversity analysis using t-SNE-based projections, and we also examine AI parameter performance from a Differential Privacy (DP) perspective. In addition, we expand our study to Explainable Artificial Intelligence (XAI)-based tuning and use SHAP and LIME methods to decipher distributed model complexity. We assess the interpretability of FL models by leveraging parameters like consistency, currentness, and compacity, which provide insight into their effectiveness and resilience in practical settings,