用随机投影压缩模型更新，实现高效私密联邦学习
FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection
- 客户端用随机矩阵将参数投影到低维空间再上传
- 通信量降低至原大小的1/100，精度与传统方法相当
- 兼顾隐私保护与通信效率，适合资源受限场景
联邦学习可在不集中数据的前提下协同训练模型，但高维参数更新易暴露敏感信息且通信开销大。本文提出FedRP，结合高斯随机投影与基于ADMM的共识优化。每轮中，客户端使用共享的隐藏随机矩阵将模型参数投影至m维空间，服务器仅聚合压缩后的表示。理论证明了投影空间内一致性与客户端模型接近性之间的高概率关联，并在参数范数有下界、ℓ₂-敏感度有界条件下，为每次发布提供了(ε,δ)-差分隐私保障。随机参数向量及投影矩阵进一步限制了常见重构攻击的信息泄露。在MNIST和CIFAR-10上使用LeNet-5和自定义卷积网络的实验表明，FedRP性能接近FedAvg，同时持续通过噪声扰动实现隐私保护。由于客户端每轮传输m而非n个值，当m≪n时，通信量可降低数量级。结果展示了准确率、隐私与通信效率间的良好权衡。代码已开源。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative model training without centralizing data, but exchanging high-dimensional updates can expose sensitive information and incur substantial communication costs. We present FedRP, a communication-efficient method combining Gaussian random projection with consensus optimization based on the alternating direction method of multipliers (ADMM). In each round, clients project their model parameters into an $m$-dimensional space using a shared random matrix hidden from the server, which aggregates only compressed representations. We establish a high-probability guarantee linking projected-space consensus to proximity among client models and derive an $(ε,δ)$-differential privacy guarantee for each release under bounded $\ell_2$-sensitivity and a positive lower bound on parameter norms. With randomized parameter vectors and the projection matrix also limits information available to common reconstruction attacks. Experiments on MNIST and CIFAR-10 with LeNet-5 and a custom convolutional network show that FedRP achieves accuracy comparable to FedAvg and consistently exposes noise-perturbed privacy via FedAvg. Because clients transmit $m$ rather than $n$ values per round, FedRP reduces communication by orders of magnitude when $m \ll n$. The results demonstrate a favorable trade-off among accuracy, privacy, and communication efficiency. Code is available at https://github.com/mhnarimani/FedRP
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。