Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks

• URL: http://arxiv.org/abs/2502.02874v1
• Date: Wed, 05 Feb 2025 04:09:15 GMT
• Title: Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks
• Authors: Fatih Temiz, Memedhe Ibrahimi, Francesco Musumeci, Claudio Passera, Massimo Tornatore,
• Abstract summary: We study the application of Federated Learning in disaggregated microwave networks for failure-cause identification.<n>Our experimental results show that VFL-based scenarios can achieve F1-Scores consistently within at most a 1% gap with respect to a centralized scenario.
• Score: 5.789459834052429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning (ML) has proven to be a promising solution to provide novel scalable and efficient fault management solutions in modern 5G-and-beyond communication networks. In the context of microwave networks, ML-based solutions have received significant attention. However, current solutions can only be applied to monolithic scenarios in which a single entity (e.g., an operator) manages the entire network. As current network architectures move towards disaggregated communication platforms in which multiple operators and vendors collaborate to achieve cost-efficient and reliable network management, new ML-based approaches for fault management must tackle the challenges of sharing business-critical information due to potential conflicts of interest. In this study, we explore the application of Federated Learning in disaggregated microwave networks for failure-cause identification using a real microwave hardware failure dataset. In particular, we investigate the application of two Vertical Federated Learning (VFL), namely using Split Neural Networks (SplitNNs) and Federated Learning based on Gradient Boosting Decision Trees (FedTree), on different multi-vendor deployment scenarios, and we compare them to a centralized scenario where data is managed by a single entity. Our experimental results show that VFL-based scenarios can achieve F1-Scores consistently within at most a 1% gap with respect to a centralized scenario, regardless of the deployment strategies or model types, while also ensuring minimal leakage of sensitive-data.

Related papers

• Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks [26.74283774805648]
  Online-score-aided federated learning (OSAFL) is designed to learn tasks relevant to wireless applications. Our simulation results on two different datasets with four popular ML models validate the effectiveness of OSAFL.
  arXiv  Detail & Related papers  (2024-08-12T01:27:06Z)
• Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments [15.757660512833006]
  In safety-critical applications, collaborative inference must be robust to significant network failures caused by environmental disruptions or extreme weather. We first formalize the problem of robust collaborative inference over a dynamic network of devices that could experience significant network faults. Then, we develop a minimalistic yet impactful method called Multiple Aggregation with Gossip Rounds and Simulated Faults (MAGS) that synthesizes simulated faults via dropout, replication, and gossiping to significantly improve robustness over baselines.
  arXiv  Detail & Related papers  (2023-12-27T17:00:09Z)
• Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity [2.6849848612544]
  Federated Learning (FL) is a framework for performing a learning task in an edge computing scenario. We propose a communication-efficient Decentralised Federated Learning (DFL) algorithm able to cope with them. Our solution allows devices communicating only with their direct neighbours to train an accurate model.
  arXiv  Detail & Related papers  (2023-12-07T18:24:19Z)
• Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks [0.0]
  The complexity and diversity of Telecom networks place an increasing strain on maintenance and operation efforts. Strict security and privacy requirements present a challenge for mobile operators to leverage network data. We propose a Bi-level Federated Graph Neural Network anomaly detection and diagnosis model.
  arXiv  Detail & Related papers  (2023-11-24T13:23:54Z)
• FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA [0.0]
  Federated Learning (FL) has emerged as a promising approach for privacy preservation. This article investigates the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss.
  arXiv  Detail & Related papers  (2023-03-29T16:36:42Z)
• Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks [49.19502459827366]
  Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-to-server (D2S) interactions.
  arXiv  Detail & Related papers  (2023-03-15T04:41:36Z)
• Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches [46.52224306624461]
  We consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed.
  arXiv  Detail & Related papers  (2023-02-23T12:29:20Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection [3.5557219875516655]
  We propose a collaborative federated learning scheme to address privacy concerns and the lack of a universal format of datasets. The proposed framework allows multiple organisations to join forces in the design, training, and evaluation of a robust ML-based network intrusion detection system.
  arXiv  Detail & Related papers  (2021-11-04T12:06:34Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks [81.83990083088345]
  We develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks. Our analysis proves that the learning performance of HFL deteriorates drastically with highly-mobile users. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm.
  arXiv  Detail & Related papers  (2021-08-20T10:46:58Z)
• Segmented Federated Learning for Adaptive Intrusion Detection System [0.6445605125467573]
  Cyberattacks cause organizations great financial, and reputation harm. Current network intrusion detection systems (NIDS) seem to be insufficent. We propose a Segmented-Federated Learning (Segmented-FL) learning scheme for a more efficient NIDS.
  arXiv  Detail & Related papers  (2021-07-02T07:47:05Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.