  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 analog 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 scales 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 $\mathcal{O}(\sqrt{\frac{1}{TN}})$ as the best known centralized algorithm
with a central parameter server. Extensive experiments demonstrate that our
algorithm DWFL also performs well in real settings.
