Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks
- URL: http://arxiv.org/abs/2609.13872v1
- Date: Sat, 12 Sep 2026 10:52:53 GMT
- Title: Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks
- Abstract summary: 6G networks operate across a dynamic, multi-domain edge-cloud continuum.<n>Security serves as the primary enabler of this decentralized paradigm.<n>This paper argues that decentralization, trustworthiness, explainability, and sustainability must be designed jointly.
- Score: 40.96834401764948
- License: http://creativecommons.org/licenses/by/4.0/
- 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
Related papers
- Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems [0.8496907566303933]
This study proposes a Cognitive Threat Intelligence and Explainable Federated Security Analytics framework for distributed infrastructure systems.<n>The proposed framework integrates Federated Learning (FL), Explainable Artificial Intelligence (XAI), and cognitive cybersecurity analytics to enable collaborative and privacy-preserving cyber threat detection.<n>To enhance intelligent threat analysis, the framework incorporates machine learning and deep learning algorithms including Random Forest, XGBoost, Autoencoder.
arXiv Detail & Related papers (2026-06-04T04:41:53Z) - Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN [62.429669462281645]
6G must transcend traditional automation to embrace agentic AI capable of perception, reasoning, and evolution.<n>This article envisions a shift from interface-bound to memory-centric architectures.<n>By replacing message passing with zero-copy observability, we empower AI agents to bridge the gap between real-time responsiveness and long-horizon context.
arXiv Detail & Related papers (2026-05-11T06:04:10Z) - 6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence [3.099103925863002]
6G networks are envisioned as AI-native infrastructures integrating communication, sensing, and computing into a unified fabric.<n>We argue for a paradigm shift toward Agentic AI-Native 6G, in which Large Language Model (LLM)-based agents operate as bounded, policy-governed reasoning entities.<n>We propose a four-layer architecture that integrates deterministic network infrastructure, semantic abstraction of intent and context, hierarchical reasoning, and a distributed multi-agent fabric.
arXiv Detail & Related papers (2026-05-02T17:24:12Z) - Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey [64.20429885919701]
We propose an energy accounting framework identifying computational and communication costs across the Perception-Reasoning-Action cycle.<n>We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware-aware inference.<n>We identify open challenges of federated green learning, carbon-aware agency, 6th generation mobile communication (6G)-native Agentic AI, and self-sustaining systems.
arXiv Detail & Related papers (2026-04-09T06:13:59Z) - A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
arXiv Detail & Related papers (2026-02-23T16:12:02Z) - Node Learning: A Framework for Adaptive, Decentralised and Collaborative Network Edge AI [0.6015898117103068]
Expansion of AI toward the edge exposes the cost and fragility of cen- tralised intelligence.<n>We introduce Node Learning, a decen- tralised learning paradigm in which intelligence resides at individual edge nodes.
arXiv Detail & Related papers (2026-02-18T19:23:47Z) - Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks [0.0]
The move to 6th Generation (6G) wireless networks creates new issues with privacy, scalability, and adaptability.<n>A new framework called the Federated Learning-based Decentralized Adaptive Intelligent Transmission Protocol (AITP) is proposed to meet these challenges.
arXiv Detail & Related papers (2025-12-20T17:18:15Z) - Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
arXiv Detail & Related papers (2025-05-26T06:44:31Z) - Decentralized Zero-Trust Framework for Digital Twin-based 6G [8.01618424103984]
The article presents a new framework that integrates the zero-trust architecture in DT-enabled 6G networks.
Unlike conventional zero-trust solutions, the proposed framework adapts a decentralized mechanism to ensure the security, privacy and authenticity of both the physical devices and their DT counterparts.
The article also discusses current solutions and future outlooks, with challenges and some technology enablers.
arXiv Detail & Related papers (2023-02-06T20:13:19Z) - Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.
In this article, we identify the key requirements and challenges of edge-native AI in 6G.
arXiv Detail & Related papers (2020-10-01T02:16:40Z)
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.