Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains. While existing approaches employ cryptographic techniques such as homomorphic encryption, differential privacy, or secure multiparty computation to mitigate inference attacks, including model inversion, membership inference, and gradient leakage, they often suffer from high computational and memory overheads. Moreover, many methods overlook the confidentiality of the global model itself, which may be proprietary and sensitive. These challenges limit the practicality of secure FL, especially in settings that involve large datasets and strict compliance requirements. We present FuSeFL, a Fully Secure and scalable FL scheme, which decentralizes training across client pairs using lightweight MPC, while confining the server's role to secure aggregation, client pairing, and routing. This design eliminates server bottlenecks, avoids full data offloading, and preserves full confidentiality of data, model, and updates throughout training. Based on our experiment, FuSeFL defends against unauthorized observation, reconstruction attacks, and inference attacks such as gradient leakage, membership inference, and inversion attacks, while achieving up to $13 \times$ speedup in training time and 50% lower server memory usage compared to our baseline.

翻译：联邦学习（FL）支持在不集中客户端数据的情况下进行协作模型训练，这使其对隐私敏感领域具有吸引力。现有方法通常采用同态加密、差分隐私或安全多方计算等密码学技术来缓解推理攻击（包括模型反演、成员推理和梯度泄漏），但它们往往存在较高的计算和内存开销。此外，许多方法忽略了全局模型本身的机密性，而该模型可能是专有且敏感的。这些挑战限制了安全联邦学习的实用性，尤其是在涉及大规模数据集和严格合规要求的场景中。本文提出FuSeFL，一种完全安全且可扩展的联邦学习方案，它利用轻量级MPC在客户端对之间分散训练，同时将服务器的角色限定为安全聚合、客户端配对和路由。该设计消除了服务器瓶颈，避免了完整数据卸载，并在整个训练过程中保持了数据、模型和更新的完全机密性。实验表明，与基线相比，FuSeFL能够防御未经授权的观察、重建攻击以及梯度泄漏、成员推理和反演攻击等推理攻击，同时实现高达$13 \times$的训练加速和50%的服务器内存使用降低。