Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules through a proper logical connector that is derived based on properties of client data, without requiring access to the raw data. In addition, the server also aggregates the local model updates with weight values determined by the quality of the clients' local data as reflected by their uploaded logic rules. The results show that LR-XFL outperforms the most relevant baseline by 1.19%, 5.81% and 5.41% in terms of classification accuracy, rule accuracy and rule fidelity, respectively. The explicit rule evaluation and expression under LR-XFL enable human experts to validate and correct the rules on the server side, hence improving the global FL model's robustness to errors. It has the potential to enhance the transparency of FL models for areas like healthcare and finance where both data privacy and explainability are important.

翻译：联邦学习（FL）是一种在保护数据隐私的同时协同训练机器学习模型的新兴方法。隐私保护的需求使得FL模型难以实现全局透明性和可解释性。为解决这一局限性，我们通过提出基于逻辑推理的可解释联邦学习（LR-XFL）方法，将基于逻辑的解释融入FL中。在LR-XFL下，FL客户端基于本地数据创建局部逻辑规则，并将其与模型更新一并发送至FL服务器。FL服务器通过一种基于客户端数据属性推导出的适当逻辑连接符连接这些局部逻辑规则，而无需访问原始数据。此外，服务器还根据客户端上传逻辑规则所反映的本地数据质量确定的权重值，对局部模型更新进行聚合。结果表明，LR-XFL在分类准确率、规则准确率和规则忠实度上分别比最相关的基线方法提升了1.19%、5.81%和5.41%。LR-XFL下的显式规则评估与表达使人类专家能够在服务器端验证和修正规则，从而提升全局FL模型对错误的鲁棒性。该方法有望增强FL模型在医疗、金融等对数据隐私与可解释性均有重要要求领域的透明度。