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Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities

Juan Marcelo Parra-Ullauri, Xunzheng Zhang, Anderson Bravalheri, Yulei Wu, Reza Nejabati, Dimitra Simeonidou

TL;DR

The paper tackles privacy-preserving analytics for 6G by advocating Federated Analytics (FA) as a distributed alternative to centralized data processing. It presents an architecture and a comprehensive taxonomy (intelligence, types, domains, methods, applications, privacy/security) to implement FA in multi-tenant, cross-domain 6G networks, and demonstrates a cross-domain orchestration scenario where FA-guided selection improves FL-based VNF profiling. A real-network prototype using the Virtual Firewall dataset shows that FA-informed client selection can reduce predictive error (MAE) for LINK capacity compared with standard FL. The work also discusses open challenges—non-IID data, synthetic data, incentive mechanisms, and decentralised FA—along with potential 6G use cases, highlighting FA’s potential to enable scalable, privacy-aware analytics and intelligent network orchestration in future hyper-connected ecosystems.

Abstract

Extensive research is underway to meet the hyper-connectivity demands of 6G networks, driven by applications like XR/VR and holographic communications, which generate substantial data requiring network-based processing, transmission, and analysis. However, adhering to diverse data privacy and security policies in the anticipated multi-domain, multi-tenancy scenarios of 6G presents a significant challenge. Federated Analytics (FA) emerges as a promising distributed computing paradigm, enabling collaborative data value generation while preserving privacy and reducing communication overhead. FA applies big data principles to manage and secure distributed heterogeneous networks, improving performance, reliability, visibility, and security without compromising data confidentiality. This paper provides a comprehensive overview of potential FA applications, domains, and types in 6G networks, elucidating analysis methods, techniques, and queries. It explores complementary approaches to enhance privacy and security in 6G networks alongside FA and discusses the challenges and prerequisites for successful FA implementation. Additionally, distinctions between FA and Federated Learning are drawn, highlighting their synergistic potential through a network orchestration scenario.

Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities

TL;DR

The paper tackles privacy-preserving analytics for 6G by advocating Federated Analytics (FA) as a distributed alternative to centralized data processing. It presents an architecture and a comprehensive taxonomy (intelligence, types, domains, methods, applications, privacy/security) to implement FA in multi-tenant, cross-domain 6G networks, and demonstrates a cross-domain orchestration scenario where FA-guided selection improves FL-based VNF profiling. A real-network prototype using the Virtual Firewall dataset shows that FA-informed client selection can reduce predictive error (MAE) for LINK capacity compared with standard FL. The work also discusses open challenges—non-IID data, synthetic data, incentive mechanisms, and decentralised FA—along with potential 6G use cases, highlighting FA’s potential to enable scalable, privacy-aware analytics and intelligent network orchestration in future hyper-connected ecosystems.

Abstract

Extensive research is underway to meet the hyper-connectivity demands of 6G networks, driven by applications like XR/VR and holographic communications, which generate substantial data requiring network-based processing, transmission, and analysis. However, adhering to diverse data privacy and security policies in the anticipated multi-domain, multi-tenancy scenarios of 6G presents a significant challenge. Federated Analytics (FA) emerges as a promising distributed computing paradigm, enabling collaborative data value generation while preserving privacy and reducing communication overhead. FA applies big data principles to manage and secure distributed heterogeneous networks, improving performance, reliability, visibility, and security without compromising data confidentiality. This paper provides a comprehensive overview of potential FA applications, domains, and types in 6G networks, elucidating analysis methods, techniques, and queries. It explores complementary approaches to enhance privacy and security in 6G networks alongside FA and discusses the challenges and prerequisites for successful FA implementation. Additionally, distinctions between FA and Federated Learning are drawn, highlighting their synergistic potential through a network orchestration scenario.
Paper Structure (29 sections, 2 equations, 5 figures, 2 tables)

This paper contains 29 sections, 2 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminaries and Related Work
  3. 6G Networks
  4. Federated Analytics and Federated Learning
  5. Related Work
  6. Federated Analytics for 6G Networks
  7. An Architecure for Federated Analytics for 6G Networks
  8. Federated Analytics for 6G: A Taxonomy
  9. Intelligence
  10. Types
  11. Domains
  12. Methods
  13. Applications
  14. Privacy and Security
  15. Example scenario: Cross-Domain Intelligent Orchestration
  16. ...and 14 more sections

Figures (5)

  • Figure 1: The estimated data volumes generated and evolution of networks towards future 6G.
  • Figure 2: Federated Analytics for 6G Architecture
  • Figure 3: Federated Analytics for 6G Taxonomy
  • Figure 4: Federated Analytics and Learning for 6G Networks: Cross-Domain Profiling
  • Figure 5: MAE for predicting LINK capacity: Standard FL vs Proposed FA-assisted FL for VNF Firewall dataset from moazzeni2020novel.