{"ID":2898086,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03952","arxiv_id":"2507.03952","title":"FedFog: Resource-Aware Federated Learning in Edge and Fog Networks","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.","short_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 intro...","url_abs":"https://arxiv.org/abs/2507.03952","url_pdf":"https://arxiv.org/pdf/2507.03952v1","authors":"[\"Somayeh Sobati-M\"]","published":"2025-07-05T08:30:37Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}