  In this paper, we tackle the network delays in the Internet of Things (IoT)
for an enhanced QoS through a stable and optimized federated fog computing
infrastructure. Network delays contribute to a decline in the
Quality-of-Service (QoS) for IoT applications and may even disrupt
time-critical functions. Our paper addresses the challenge of establishing fog
federations, which are designed to enhance QoS. However, instabilities within
these federations can lead to the withdrawal of providers, thereby diminishing
federation profitability and expected QoS. Additionally, the techniques used to
form federations could potentially pose data leakage risks to end-users whose
data is involved in the process. In response, we propose a stable and
comprehensive federated fog architecture that considers federated network
profiling of the environment to enhance the QoS for IoT applications. This
paper introduces a decentralized evolutionary game theoretic algorithm built on
top of a Genetic Algorithm mechanism that addresses the fog federation
formation issue. Furthermore, we present a decentralized federated learning
algorithm that predicts the QoS between fog servers without the need to expose
users' location to external entities. Such a predictor module enhances the
decision-making process when allocating resources during the federation
formation phases without exposing the data privacy of the users/servers.
Notably, our approach demonstrates superior stability and improved QoS when
compared to other benchmark approaches.
