  With the increasing number of data collectors such as smartphones, immense
amounts of data are available. Federated learning was developed to allow for
distributed learning on a massive scale whilst still protecting each users'
privacy. This privacy is claimed by the notion that the centralized server does
not have any access to a client's data, solely the client's model update. In
this paper, we evaluate a novel attack method within regular federated learning
which we name the First Dense Layer Attack (Fidel). The methodology of using
this attack is discussed, and as a proof of viability we show how this attack
method can be used to great effect for densely connected networks and
convolutional neural networks. We evaluate some key design decisions and show
that the usage of ReLu and Dropout are detrimental to the privacy of a client's
local dataset. We show how to recover on average twenty out of thirty private
data samples from a client's model update employing a fully connected neural
network with very little computational resources required. Similarly, we show
that over thirteen out of twenty samples can be recovered from a convolutional
neural network update. An open source implementation of this attack can be
found here https://github.com/Davidenthoven/Fidel-Reconstruction-Demo
