用哈希压缩梯度，防攻击又省通信。
LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping
- 用局部敏感哈希将梯度转为不可逆二进制码
- 50%恶意节点下仍保持模型性能，通信量降1000倍
- 适合高风险环境下的高效安全联邦学习
联邦学习（FL）可在不暴露原始数据的前提下实现分布式协同训练，但其去中心化特性在信任缺失环境中易受威胁。推理攻击可能从梯度更新中恢复敏感信息，而投毒攻击可降低模型性能或诱导恶意行为。现有防御方法常伴随高通信与计算开销，或检测精度有限。为此，我们提出LSHFed框架，通过引入新型梯度验证机制LSHGM，利用多超平面局部敏感哈希将高维梯度映射为紧凑二进制表示，仅凭不可逆哈希形式即可精准识别并过滤恶意梯度，显著降低隐私泄露风险并大幅减少传输开销。大量实验表明，即便高达50%的参与者为合谋攻击者，LSHFed仍能维持高模型性能，且相比全梯度方法，梯度验证通信量最高降低1000倍。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative model training across distributed nodes without exposing raw data, but its decentralized nature makes it vulnerable in trust-deficient environments. Inference attacks may recover sensitive information from gradient updates, while poisoning attacks can degrade model performance or induce malicious behaviors. Existing defenses often suffer from high communication and computation costs, or limited detection precision. To address these issues, we propose LSHFed, a robust and communication-efficient FL framework that simultaneously enhances aggregation robustness and privacy preservation. At its core, LSHFed incorporates LSHGM, a novel gradient verification mechanism that projects high-dimensional gradients into compact binary representations via multi-hyperplane locally-sensitive hashing. This enables accurate detection and filtering of malicious gradients using only their irreversible hash forms, thus mitigating privacy leakage risks and substantially reducing transmission overhead. Extensive experiments demonstrate that LSHFed maintains high model performance even when up to 50% of participants are collusive adversaries while achieving up to a 1000x reduction in gradient verification communication compared to full-gradient methods.
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