Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic Analysis - FLIP4 框架

Aug 5, 2026

Upload by Maeve

IoT traffic analysis increasingly relies on machine learning, but 5G URLLC requirements demand low-latency detection and mitigation while avoiding privacy and service disruption. Distributed intelligence on edge reduces centralization yet introduces delays and privacy risks, and federated learning improves privacy without meeting strict latency constraints. This work proposes FLIP4, a resource-efficient federated in-network framework using lightweight federated tree-based models deployed inside network devices. FLIP4 lowers resource and communication overhead, enables prompt false-alert mitigation via metering and dropping, and balances learning accuracy with privacy needs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

Federated learning and latency/privacy challenges

In-network ML inference for edge devices

Prior work limitations

Problem statement and proposed solution (FLIP4)

# Federated In-Network Machine Learning for Privacy-Preserving loT Traffic Analysis MINGYUAN ZANG,Technical University of Denmark,DenmarkCHANGGANG ZHENG,University of Oxford,UKTOMASZ KOZIAK,Netlight,DenmarkNOA ZILBERMAN,University of Oxford,UKLARS DITTMANN,Technical University of Denmark,Denmark The expanding use of IoT has driven machine learning(ML)based traffic analysis.5G networks'standards,requiring low-latency communications for time-critical services,pose new challenges to traffic analysis.They necessitate fast analysis and response,preventing service disruption or security impact on networkinfrastructure.Distributed intelligence on IoT edge has been studied to analyze traffic,but introduces delaysand raises privacy concerns.Federated learning can address privacy concerns,but does not meet latencyrequirements.In this paper,we propose FLIP4:an efficient federated learning-based framework for in-networktraffic analysis.Our solution introduces a lightweight federated tree-based model,offloaded and runningwithin network devices.FLIP4 consumes less resources than previous solutions and reduces communicationoverheads,making it well-suited for IoT edge traffic analysis.It ensures prompt mitigation and minimal impacton services in the presence of false alerts using two approaches(metering and dropping),thereby balancinglearning accuracy and privacy requirements. ## 1 INTRODUCTION Distributed intelligence has been increasingly recognized for its scalability and flexibility comparedto centralized intelligence,especially in the context of IoT networks.Studies have highlighted itseffectiveness in providing optimal solutions for collective information and holistic views in IoTnetworks [33,42].It has been applied for traffic analysis services in IoT networks,such as deviceidentification[13]and anomaly detection[55].However,existing distributed intelligence-basedsolutions [6,52]primarily focus on accurate analysis and decisions.These solutions,while effective,fall short in fast response and mitigation following decision-making,as well as in ensuring efficientcommunication among distributed nodes. This limitation becomes particularly critical as networks evolve to support ultra-reliable andlow-latency communication(URLLC)[10].In such environments,the ability to quickly respond toincidents is crucial,given that emerging attacks can significantly impact network infrastructure ifnot promptly addressed.This risk is notably high in IoT networks,where end devices often lacksecurity measures due to resource constraints and performance considerations [48].Consequently,there is a pressing need for active defense services to detect and mitigate attacks effectively andquickly[8]. Federated Learning(FL)has provided a solution to collaboratively train and improve distributedintelligent models over time.It introduces distributed machine learning-based deployment acrossmultiple distributed nodes while keeping the data localized.Despite the demonstrated efficiencyand privacy in traffic analysis,FL has design challenges in high communication overhead andunstable connection oflocal nodes [57].This affects the performance of the FL-based traffic analysisin IoT networks where network edge devices are relatively dynamic and have limited computingresources. Minimizing the action enforcement time for identified anomalies remains another challenge ofapplying FL-based traffic analysis for time-critical services.The emerging attacks have necessitatedfast mitigation responses,as recent reports indicate a significant increase in DDoS and Botnetattacks in IoT networks,exploiting protocol vulnerabilities in IoT devices [2].Prior work [11,33]has explored FL to enable accurate traffic analysis but falls short in fast response and actionenforcement.Providing ML-based analysis at line-rate within IoT edge network devices,as trafficis forwarded through the devices,can potentially ensure quick reactions to identified traffic issues.This opportunity has been

FAQs

为什么传统的边缘分布式智能和单纯的联邦学习难以满足IoT流量分析的需求？

边缘分布式智能可能引入延迟并带来隐私担忧；而联邦学习虽然能提升隐私，但仍面临高通信开销与本地连接不稳定等问题，难以满足时延要求。

FLIP4通过什么方式实现低时延的端到端流量分析与处置？

FLIP4采用轻量化的联邦树模型，并将推理放在网络设备的数据面以进行原位分析，从而实现对异常的快速响应与缓解。

当出现误报时，FLIP4如何在准确率与隐私之间取得平衡？

FLIP4使用两种处置策略（metering与dropping）对误报进行快速缓解，从而在学习精度与隐私要求之间实现平衡。

Aug 5, 2026

Upload by Maeve