航空装备的安全运行对保障飞行安全具有重要意义，而航空发动机作为其核心部件，其故障诊断研究具有重要价值。然而，航空发动机运行数据通常由不同机构分别持有，受保密性与隐私保护限制难以集中共享，形成数据孤岛问题。联邦学习通过在各节点本地训练模型并进行参数聚合，可在不共享原始数据的条件下实现全局模型训练，为分布式航空发动机数据环境下的故障诊断提供了一种有效途径。然而，多源航空发动机数据通常呈现非独立同分布特性，容易导致联邦模型性能下降。针对该问题，提出一种基于Gap度量的联邦学习故障诊断方法，通过Gap几何度量评估各客户端模型贡献，从而提升非均衡数据环境下的诊断性能。实验采用航空发动机数据集，并与独立训练、FedAvg和FedProx方法进行对比。结果表明，所提出方法在分布式数据环境下具有较好的诊断性能与稳定性。

The safe operation of aviation equipment is essential for flight safety, and aero-engine fault diagnosis is of great importance because the engine is the core power unit of aircraft. In practical engineering scenarios, monitoring data of aero-engines are usually held by different institutions or test platforms. Due to confidentiality and privacy constraints, such data are difficult to collect centrally, resulting in a typical data-island problem. Federated learning provides a feasible way to train a global diagnostic model through local model updating and server-side parameter aggregation without sharing raw data. However, multi-source aero-engine data often follow non-independent and non-identically distributed patterns, which may reduce the performance and stability of conventional federated models. To address this problem, this paper proposes a Gap-metric-based federated aggregation method for distributed aero-engine fault diagnosis. The proposed method evaluates client contributions from the geometric structure of the feature space by considering intra-class compactness and inter-class separability, and then adaptively adjusts aggregation weights. Experiments on an aero-engine dataset, compared with Direct CNN, FedAvg, and FedProx, demonstrate that the proposed method achieves better diagnostic accuracy and stable training performance in distributed non-IID scenarios.