Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth. To address these challenges, in this work we formulate a novel constrained optimization problem, and propose an FL framework harnessing wireless channel perturbations and interference for improving privacy, bandwidth-efficiency, and scalability. The resultant algorithm is coined analog federated ADMM (A-FADMM) based on analog transmissions and the alternating direction method of multipliers (ADMM). In A-FADMM, all workers upload their model updates to the parameter server (PS) using a single channel via analog transmissions, during which all models are perturbed and aggregated over-the-air. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper including the honest-but-curious PS, thereby preserving data privacy against model inversion attacks. We formally prove the convergence and privacy guarantees of A-FADMM for convex functions under time-varying channels, and numerically show the effectiveness of A-FADMM under noisy channels and stochastic non-convex functions, in terms of convergence speed and scalability, as well as communication bandwidth and energy efficiency.

中文翻译：

---

利用无线通道实现可扩展和保护隐私的联合学习

无线连接有助于实现可扩展的联邦学习 (FL)，但无线通道给模型训练带来了挑战，其中通道随机性会扰乱每个工作人员的模型更新，而多个工作人员的更新在有限带宽下会产生显着干扰。为了应对这些挑战，在这项工作中，我们制定了一个新的约束优化问题，并提出了一个 FL 框架，利用无线信道扰动和干扰来提高隐私、带宽效率和可扩展性。由此产生的算法是基于模拟传输和乘法器交替方向法 (ADMM) 创造的模拟联合 ADMM (A-FADMM)。在 A-FADMM 中，所有工作人员通过模拟传输使用单个通道将他们的模型更新上传到参数服务器 (PS)，在此期间，所有模型都在空中进行扰动和聚合。这不仅节省了通信带宽，而且对任何窃听者（包括诚实但好奇的 PS）隐藏了每个工人的确切模型更新轨迹，从而保护数据隐私免受模型反转攻击。我们正式证明了 A-FADMM 在时变通道下凸函数的收敛性和隐私保证，并数值显示了 A-FADMM 在噪声通道和随机非凸函数下的收敛速度和可扩展性方面的有效性，以及作为通信带宽和能源效率。从而保护数据隐私免受模型反转攻击。我们正式证明了 A-FADMM 在时变通道下凸函数的收敛性和隐私保证，并数值显示了 A-FADMM 在噪声通道和随机非凸函数下的收敛速度和可扩展性方面的有效性，以及作为通信带宽和能源效率。从而保护数据隐私免受模型反转攻击。我们正式证明了 A-FADMM 在时变通道下凸函数的收敛性和隐私保证，并数值显示了 A-FADMM 在噪声通道和随机非凸函数下的收敛速度和可扩展性方面的有效性，以及作为通信带宽和能源效率。