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

arXiv: https://arxiv.org/abs/2507.03952 · HTML: https://mlsanity.com/paper/2507.03952/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation](https://mlsanity.com/paper/2603.08735/) (2026)

## Abstract (arXiv)

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.