In low-altitude wireless networks (LAWN), federated learning (FL) enables collaborative intelligence among unmanned aerial vehicles (UAVs) and integrated sensing and communication (ISAC) devices while keeping raw sensing data local. Due to the "right to be forgotten" requirements and the high mobility of ISAC devices that frequently enter or leave the coverage region of UAV-assisted servers, the influence of departing devices must be removed from trained models. This necessity motivates the adoption of federated unlearning (FUL) to eliminate historical device contributions from the global model in LAWN. However, existing FUL approaches implicitly assume that the UAV-assisted server executes unlearning operations honestly. Without client-verifiable guarantees, an untrusted server may retain residual device information, leading to potential privacy leakage and undermining trust. To address this issue, we propose VerFU, a privacy-preserving and client-verifiable federated unlearning framework designed for LAWN. It empowers ISAC devices to validate the server-side unlearning operations without relying on original data samples. By integrating linear homomorphic hash (LHH) with commitment schemes, VerFU constructs tamper-proof records of historical updates. ISAC devices ensure the integrity of unlearning results by verifying decommitment parameters and utilizing the linear composability of LHH to check whether the global model accurately removes their historical contributions. Furthermore, VerFU is capable of efficiently processing parallel unlearning requests and verification from multiple ISAC devices. Experimental results demonstrate that our framework efficiently preserves model utility post-unlearning while maintaining low communication and verification overhead.

翻译：在低空无线网络（LAWN）中，联邦学习（FL）使无人机（UAV）与集成感知与通信（ISAC）设备能够在保持原始感知数据本地化的同时实现协同智能。由于"被遗忘权"的要求以及频繁进出无人机辅助服务器覆盖区域的高移动性ISAC设备，必须从训练模型中移除离网设备的影响。这一需求促使采用联邦遗忘（FUL）技术，以消除LAWN中全球模型中历史设备贡献的影响。然而，现有FUL方法隐含假设无人机辅助服务器诚实地执行遗忘操作。在缺乏客户端可验证保证的情况下，不可信服务器可能保留残余设备信息，导致隐私泄露并破坏信任。为解决这一问题，我们提出VerFU——一种专为LAWN设计的隐私保护且客户端可验证的联邦遗忘框架。该框架使ISAC设备无需依赖原始数据样本即可验证服务器端的遗忘操作。通过将线性同态哈希（LHH）与承诺方案相结合，VerFU构建了历史更新的防篡改记录。ISAC设备通过验证解承诺参数并利用LHH的线性可组合性检查全局模型是否准确移除其历史贡献，从而确保遗忘结果的完整性。此外，VerFU能够高效处理来自多个ISAC设备的并行遗忘请求与验证。实验结果表明，我们的框架在保持模型效用后遗忘的同时，维持了较低的通信与验证开销。