Privacy-Preserving Distributed Learning Framework for 6G Telecom Ecosystems
We present a privacy-preserving distributed learning framework for telecom ecosystems in the 6G-era that enables the vision of shared ownership and governance of ML models, while protecting the privacy of the data owners. We demonstrate its benefits by applying it to the use-case of Quality of Transmission (QoT) estimation in multi-domain multi-vendor optical networks, where no data of individual domains is shared with the network management system (NMS).
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementPrivacy PreservingSimilar Papers 제목 키워드 기반
A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privac…
Intrusion DetectionFederated LearningAgentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines
The rapid proliferation of synthetic media, presentation attacks, and document forgeries has created significant vulnerabilities in Know Your Customer (KYC) workflows across financial services, telecommunications, and di…
DeepFake DetectionFraud DetectionPrivacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap
The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard sensitive data generated by large-scale, heterogeneous, and resource-constra…
Federated LearningTest Code Generation for Telecom Software Systems using Two-Stage Generative Model
In recent years, the evolution of Telecom towards achieving intelligent, autonomous, and open networks has led to an increasingly complex Telecom Software system, supporting various heterogeneous deployment scenarios, wi…
Code GenerationLanguage ModelingLanguage ModellingLarge Language Model+1Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry
Data is the main fuel of a successful machine learning model. A dataset may contain sensitive individual records e.g. personal health records, financial data, industrial information, etc. Training a model using this sens…
Cloud ComputingPredictionPrivacy Preserving