FedFog: Resource-Aware Federated Learning in Edge and Fog Networks

Somayeh Sobati-M

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arXiv · 2507.03952

FedFog: Resource-Aware Federated Learning in Edge and Fog Networks

Somayeh Sobati-M

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Abstract

As edge and fog computing become central to modern distributed systems, there's growing interest in combining serverless architectures with privacy-preserving machine learning techniques like federated learning (FL). However, current simulation tools fail to capture this integration effectively. In this paper, we introduce FedFog, a simulation framework that extends the FogFaaS environment to support FL-aware serverless execution across edge-fog infrastructures. FedFog incorporates an adaptive FL scheduler, privacy-respecting data flow, and resource-aware orchestration to emulate realistic, dynamic conditions in IoT-driven scenarios. Through extensive simulations on benchmark datasets, we demonstrate that FedFog accelerates model convergence, reduces latency, and improves energy efficiency compared to conventional FL or FaaS setups-making it a valuable tool for researchers exploring scalable, intelligent edge systems.

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BibTeX RIS

Somayeh Sobati-M. 2025-07-05. FedFog: Resource-Aware Federated Learning in Edge and Fog Networks. https://arxiv.org/abs/2507.03952

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