联邦可解释人工智能：角色、架构、评估及开放挑战
Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges
- Department of Information Engineering, University of Pisa（比萨大学信息工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文综述联邦可解释人工智能（FedXAI）范式，介绍其分类法，回顾多种方法及评估实践，讨论不足，识别如非IID数据下可解释性等关键挑战，为设计可信、透明且隐私保护的联邦人工智能系统提供参考。
AI中文摘要：
联邦学习（FL）已成为跨分布式和异构数据源进行隐私保护协作模型训练的关键范式。它解决了数据保密问题，但未解决现代机器学习模型的不透明性。同时，可解释人工智能（XAI）因提高透明度等受到关注。二者结合产生了联邦可解释人工智能（FedXAI）范式。本综述系统回顾FedXAI，强调可解释性从事后工具到FL生命周期组成部分的转变，介绍分类法，回顾多种方法，审视评估实践，讨论相关不足，最后识别关键挑战，为设计可信、透明和隐私保护的联邦人工智能系统提供参考框架。
英文摘要：
Federated Learning (FL) has emerged as a key paradigm for privacy-preserving collaborative model training across distributed and heterogeneous data sources. By keeping raw data local, FL addresses data confidentiality concerns, yet it does not resolve the opacity of modern machine learning models. In parallel, Explainable Artificial Intelligence (XAI) has gained attention for improving transparency, trust, and accountability, particularly in high-stakes domains. Their intersection has given rise to Federated Explainable Artificial Intelligence (FedXAI) paradigm, which aims to jointly satisfy privacy and explainability requirements. This survey provides a systematic review of FedXAI, highlighting the transition of explainability from a post-hoc tool to an integral component of the FL lifecycle. We show how explainability supports aggregation, personalization, robustness, coordination, and system-level decision making. To organize the literature, we introduce a taxonomy that classifies FedXAI methods by the role of explainability, model and explainer types, explanation scope, integration level, FL settings, and data heterogeneity. We review approaches ranging from model-agnostic explanations to interpretable federated models and explainability-aware aggregation mechanisms. We also examine evaluation practices and discuss the lack of standardized benchmarks and metrics for measuring explanation quality, stability, privacy leakage, and computational overhead. Finally, we identify key challenges, including explainability under non-IID data, explanation-centric security threats, communication-efficient XAI, continual FedXAI, and the integration of domain knowledge and regulatory constraints. By consolidating existing work and identifying key gaps, this survey serves as a reference framework for designing trustworthy, transparent, and privacy-preserving federated AI systems.
