Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan, Mohammad; Mohjazi, Lina; Bariah, Lina; Centeno, Anthony; Zoha, Ahmed; Muhaidat, Sami; Debbah, Mérouane; Imran, Muhammad Ali

doi:10.48550/arxiv.2111.07392

Cited by 7 publications

(9 citation statements)

References 180 publications

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“…The authors also discussed the reinforcement learning model for performance estimation of the proposed framework and finally discussed a potential solution to the open problems of U2X communication. Authors in [51] provide state-of-the-art applications of FL in B5G/6G wireless technologies based on performance metrics, highlight the FL operational challenges, and provide solutions to important networking areas such as cellular, IoV, UAV, re-configurable intelligent surfaces, and IoT, etc.…”

Section: B State-of-the-artmentioning

confidence: 99%

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

et al. 2022

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Recently, unmanned aerial vehicles (UAVs) have gained attention due to increased use-cases in healthcare, monitoring, surveillance, and logistics operations. UAVs mainly communicate with mobile base stations, ground stations (GS), or networked peer UAVs, known as UAV swarms. UAVs communicate with GS, or UAV swarms, over wireless channels to support mission-critical operations. Communication latency, bandwidth, and precision are of prime importance in such operations. With the rise of data-driven applications, fifth-generation (5G) networks would face bottlenecks to communicate at near-real-time, at low latency and improved coverage. Thus, researchers have shifted towards network designs that incorporate beyond 5G (B5G) networks for UAV designs. However, UAVs are resource-constrained, with limited power and battery, and thus centralized cloud-centric models are not suitable. Moreover, as exchanged data is through open channels, privacy and security issues exist. Federated learning (FL) allows data to be trained on local nodes, preserving privacy and improving network communication. However, sharing of local updates is required through a trusted consensus mechanism. Thus, blockchain (BC)-based FL schemes for UAVs allow trusted exchange of FL updates among UAV swarms and GS. To date, limited research has been carried out on the integration of BC and FL in UAV management. The proposed survey addresses the gap and presents a solution taxonomy of BC-based FL in UAVs for B5G networks due to the open problem. This paper presents a reference architecture and compares its potential benefits over traditional BC-based UAV networks. Open issues and challenges are discussed, with possible future directions. Finally, a logistics case study of BC-based FL-oriented UAVs in 6G networks is presented. The survey aims to aid researchers in developing potential UAV solutions with the key integrating principles over a diverse set of application verticals.INDEX TERMS Beyond 5G networks, 6G, blockchain, federated learning, unmanned aerial vehicles. D. ORGANIZATION AND READING MAP

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Section: B State-of-the-artmentioning

confidence: 99%

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

et al. 2022

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“…[13]- [15] EC fields Some overviews of the concepts, applications, opportunities and challenges of EC [16], [17] EI fields Some research efforts on EI with architectures, frameworks, guidances and key technologies [18]- [25] BC fields Some comprehensive surveys on BC from specific aspects [26] 5G and BC A survey on the potential of BC for enabling key 5G technologies [27] Cloud of Things and BC An indepth survey on the applications integrating Cloud of Things and BC [28] EC and BC A review on the integration of EC and BC systems with related research challenges [29] FL and BC A survey on the FL architecture with BC [30] AI and BC A comprehensive review on how to utilize BC to facilitate AI applications [31] AI and BC A brief survey on the integration of BC and AI in various applications [32] ML and BC A convergence of BC and ML for communications and networking systems [33] ML and BC A systematic review on the integration of ML and BC applications…”

Section: Ref Topic Main Contributionmentioning

confidence: 99%

“…With the breakthroughs of AI, some decentralized AI algorithms assisted by BC have attracted considerable interest [29]- [33]. Driven by federated learning (FL), the authors in [29] discuss existing FL architecture, enabling technologies with BC, and highlight their implications to future FL algorithms, whereas [30] performs a comprehensive review of how to utilize BC to facilitate AI applications. Further, D. C. Nguyen et al survey the latest research efforts on the integration of BC and AI for fighting COVID-19 in various applications [31].…”

Section: Our Work Ei and Bcmentioning

confidence: 99%

Integrating Edge Intelligence and Blockchain: What, Why, and How

Wang

Ren

Qiu

et al. 2022

IEEE Commun. Surv. Tutorials

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Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas.Index Terms-Edge intelligence (EI), blockchain (BC), edge computing, decentralization, distributed ledger technology (DLT).

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“…However, each IoT device can generate or collect very small amounts of data that are not enough for training any highly efficient AI model. Federated ML can overcome this limitation in smart IoT systems which handle huge training data at multiple edge devices [58][59]. The key benefits of FL are explained below.…”

Section: A Fundamentalsmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Cited by 7 publications

References 180 publications

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

Integrating Edge Intelligence and Blockchain: What, Why, and How

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

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