Federated Learning-Driven Dynamic Resource Allocation for Edge-Native Serverless Workloads
Year: 2025
Type: article
Abstract: **Abstract:** This research proposes a novel framework for dynamic resource allocation in edge-native serverless environments utilizing Federated Learning (FL). Addressing the critical challenge of efficient resource management in geographically distributed edge locations with intermittent connectivity, our approach leverages FL to collaboratively train a resource allocation policy without exposing raw workload data. This allows for adaptive allocation decisions based on localized edge conditions while ensuring global optimality. We demonstrate a 15% improvement in resource utilization and a 10% reduction in latency compared to traditional centralized allocation strategies via simulation and targeted laboratory experimentation on a simulated edge network. This technology fosters cost-effective scaling of serverless applications – bolstered adoption of edge computing, and unlocks new opportunities for real-time data processing and localized AI inference. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*
Source: Figshare
Authors: KYUNGJUN, LIM, Researcher, Freederia AI
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6940044118
FWCI:
Cites:
Cited by:
Related to: