Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges

Privacy and transparency can work together: Federated Learning protects raw data by keeping it local, but this doesn't make AI models transparent. FedXAI combines both approaches so organizations can train collaborative AI systems that are both private and explainable—addressing two critical concerns simultaneously.

Explainability is becoming essential, not optional: The paper shows explainability is transitioning "from a post-hoc tool to an integral component of the FL lifecycle," meaning explanations should be built into federated systems from the start rather than added afterward. This supports better decision-making around model aggregation, personalization, and robustness.

We lack standardized ways to measure explanation quality: A major finding is "the lack of standardized benchmarks and metrics for measuring explanation quality, stability, privacy leakage, and computational overhead." This means organizations currently have no consistent way to verify whether their federated AI systems are truly explainable or trustworthy.

Explainability serves multiple practical functions: Beyond transparency, the paper identifies that explainability supports critical system functions including aggregation (combining models), personalization (tailoring to different users), robustness (detecting attacks), and coordination across distributed partners—making it operationally valuable, not just ethically important.

The field still faces significant open challenges: The survey identifies gaps in evaluation practices and standardization, suggesting that while FedXAI is emerging as important for responsible AI, the field needs more development before organizations can reliably deploy these systems in high-stakes domains.