发表机构
Hanyang University（汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对基于符号的联邦学习通信成本高、易受攻击等问题，提出轻量级信息理论安全聚合框架，通过单轮安全乘法计算MV多项式，引入逆形式指数约简和单轮安全乘法技术，减少通信和延迟，实现抗数据丢失和对抗行为，为安全聚合奠定实践基础。
AI 中文摘要
设备端联邦学习能够在智能手机和物联网节点等资源受限设备上进行隐私保护和个性化模型训练。为降低通信成本，基于符号的方法（如signSGD）传输单比特梯度，但暴露梯度符号易受推理攻击，且现有安全聚合方案与之不兼容或开销大。我们提出为基于符号的联邦学习量身定制的轻量级信息理论安全聚合框架。通过单轮安全乘法安全计算多数投票（MV）多项式，在诚实多数假设下确保端到端信息理论安全，仅向服务器揭示最终聚合符号。引入逆形式指数约简和单轮安全乘法两种关键技术，相比传统方法，在线通信最多减少99.5%，延迟最多减少85.7%，还利用基于MDS码的解码实现抗数据丢失和对抗行为，准确率分别提高最多20.65%和10.74%。该框架为基于符号的联邦学习中的大规模、低延迟和信息理论安全聚合奠定了实践基础。
英文摘要
On-device federated learning (FL) enables privacy-preserving and personalized model training on resource-constrained devices such as smartphones and IoT nodes. To reduce communication cost, sign-based methods (e.g., signSGD) transmit one-bit gradients. However, exposing gradient signs makes them vulnerable to inference attacks, while existing secure aggregation schemes are often incompatible with such methods or incur significant computational and communication overhead. We propose a lightweight and information-theoretically secure aggregation framework tailored for sign-based FL. The framework securely computes the majority vote (MV) polynomial through single-round secure multiplication, ensuring end-to-end information-theoretic security under the honest-majority assumption while revealing only the final aggregated sign to the server. To enhance efficiency and scalability, we introduce two key techniques. First, inverse-form exponent reduction halves the effective MV polynomial degree, reducing both communication and computation costs. Second, we propose single-round secure multiplication, achieving linear offline complexity and storage with only a single online communication. Together, these techniques reduce online communication by up to 99.5% and latency by up to 85.7% compared to conventional approaches. Also, by leveraging inherent MDS-code-based decoding, the framework achieves robustness against both dropouts and adversarial behaviors, yielding accuracy gains of up to 20.65% and 10.74%, respectively. Overall, the proposed framework establishes a practical foundation for large-scale, low-latency, and information-theoretically secure aggregation in sign-based FL.
