Decentralized intelligence boosts security and trust in 6G networks
Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks
Networking and Internet ArchitectureArtificial Intelligence
Summary
As 6G networks develop, managing all the data in one place causes delays and risks. The paper explains how spreading out AI tasks across many devices and network parts can help. This approach keeps networks safer and faster by using local information quickly. The authors also say that security, trust, clear explanations, and saving energy must all be considered together when designing these systems.
What this means in practice
- •For network security teams: Implement local AI-based threat detection and attack response using decentralized data without relying on central servers.
- •For telecommunications operators: Design 6G network management that jointly ensures decentralized operation, trust, transparency, and energy efficiency.
A position paper. It proposes an approach and reports no results.
Authors
Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán, Fernando Torres-Vega, José María Jorquera Valero, Manuel Gil Pérez, Eunjeong Jeong, Nikolaos Pappas, Farah Abed Zadeh, Chamara Sandeepa, Bartlomiej Siniarski, Madhusanka Liyanage, Betül Güvenç Paltun, Leyli Karaçay, Ioannis Pitsiorlas, Marios Kountouris
Abstract
As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G networks operate across a dynamic, multi-domain edge-cloud continuum where data originates from heterogeneous sources including user devices, radio access networks, sensing infrastructures, and vertical applications. Centralizing this massive volume of data creates severe communication overhead, unacceptable latency bottlenecks, single points of failure, and complex cross-domain governance challenges. Consequently, decentralization becomes a fundamental architectural requirement for future 6G network intelligence and zero-touch operations. Security serves as the primary enabler of this decentralized paradigm. Critical security functions, such as real-time threat detection, physical-layer attack mitigation, slice protection, and intrusion detection, require immediate access to local context and telemetry before operational data loses its value. However, moving intelligence to the edge via collaborative paradigms like federated learning (FL) and decentralized FL (DFL) introduces complex trade-offs. System security cannot be addressed in isolation; it is deeply intertwined with equally important aspects like trustworthiness, explainability, and energy sustainability. Taking these aspects into account, this paper develops a unified perspective on decentralized intelligence for 6G, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements