标题： FuSeFL：完全安全且可扩展的跨库联邦学习 显示英文标题

标题： FuSeFL: Fully Secure and Scalable Cross-Silo Federated Learning

摘要： 联邦学习（FL）使协作模型训练成为可能，而无需集中客户端数据，这使其在隐私敏感领域具有吸引力。 虽然现有方法采用密码技术，如同态加密、差分隐私或安全多方计算来缓解推理攻击——包括模型逆向、成员推理和梯度泄露——但它们通常会遭受高计算、通信或内存开销的问题。 此外，许多方法忽视了全局模型本身的机密性，这可能是专有且敏感的。 这些挑战限制了安全联邦学习的实际应用，尤其是在涉及大规模数据集和严格合规要求的跨库部署中。 我们提出了FuSeFL，这是一种完全安全且可扩展的联邦学习方案，专为跨库环境设计。 FuSeFL使用轻量级安全多方计算（MPC）在客户端对之间分散训练，同时将服务器的角色限制为安全聚合。 这种设计消除了服务器瓶颈，避免了数据卸载，并在整个训练过程中保持数据、模型和更新的完全机密性。 FuSeFL能够抵御推理威胁，实现高达95%的通信延迟降低和50%的服务器内存使用减少，并在准确性上优于之前的保密联邦学习解决方案，证明了其在规模上的强大安全性和效率。 显示英文摘要

摘要： 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, communication, or 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 cross-silo deployments involving large datasets and strict compliance requirements. We present FuSeFL, a fully secure and scalable FL scheme designed for cross-silo settings. FuSeFL decentralizes training across client pairs using lightweight secure multiparty computation (MPC), while confining the server's role to secure aggregation. This design eliminates server bottlenecks, avoids data offloading, and preserves full confidentiality of data, model, and updates throughout training. FuSeFL defends against inference threats, achieves up to 95% lower communication latency and 50% lower server memory usage, and improves accuracy over prior secure FL solutions, demonstrating strong security and efficiency at scale.