Federated Learning and Evolutionary Game Model for Fog Federation Formation

TL;DR

This work tackles IoT QoS delays by forming stable fog federations using a decentralized, privacy-preserving framework. It combines a federated learning QoS predictor with a GA-based federation formation and an evolutionary game theory stabilizer to achieve stable, profitable, and privacy-aware federation structures. Empirical results show the approach delivers superior stability, higher QoS (lower latency and higher throughput), and better federation payoffs compared with baselines. The decentralized brokerless architecture reduces data leakage risks while maintaining effective resource sharing and near-optimal federation configurations, enhancing practical impact for IoT-edge ecosystems.

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.

Figures (9)

• Figure 1: Novel Federated Fog Architecture Formation
• Figure 2: Federated Learning Illustration
• Figure 3: Evolutionary game theory flowchart
• Figure 4: Training Metrics of the Response Time Model
• Figure 5: Training Metrics of the Throughput Model