This paper studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a Decentralized-Wireless-Federated-Learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the anolog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies $(\epsilon,\delta)$-differential privacy and the privacy budget per worker scale as $\mathcal{O}(\frac{1}{\sqrt{N}})$, in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of $\sqrt{\frac{1}{TN}}$ as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.
翻译:本文研究无线 IoT 网络中分散化的联邦学习算法。 用于联合学习的传统参数服务器结构面临一些问题, 如低过错容忍度、大型通信管理费和私人数据无法获取等。 为了解决这些问题,我们建议采用名为 DWFL 的分散化- 无线联- 学习算法。 该算法在工人以同侪和无服务器方式组织起来的系统中起作用, 工人将其隐私保护数据与无线频道的肛门传输计划同时交换。 经过严格分析, 我们显示 DWFL 满足了$( epslon,\delta) 差异性隐私和每个工人的隐私预算, 如 $\ mathcal{O} (\ frac{ 1unsqrt{N ⁇ ) 。 。 与正方位传输方法的常态预算不同。 此外, DWFL 以 $qrtrt {1\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\