Federated Learning and Evolutionary Game Model for Fog Federation Formation

TL;DR

This paper proposes a novel decentralized federated fog architecture utilizing evolutionary game theory and genetic algorithms to improve QoS and stability in fog federations for IoT, while preserving data privacy.

Contribution

It introduces a new federated fog formation method combining evolutionary game theory and federated learning to enhance stability, QoS, and privacy in fog federations.

Findings

Demonstrates superior stability over benchmark methods.

Achieves improved QoS in fog federation formation.

Ensures data privacy through decentralized QoS prediction.

Abstract

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.