Federated learning for 6G communications: Challenges, methods, and future directions

By Yi Liu, Xingliang Yuan, Zehui Xiong, Jiawen Kang, Xiaofei Wang, Dusist Niyato. China Communications, 2020

331 citations recorded by the source corpus.

As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally believed that 6G will be established on ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning (ML) solutions in heterogeneous and massive-scale networks. However, traditional ML techniques require centralized data collection and processing by a central server, which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns. Federated learning, as an emerging distributed AI approach with privacy preservation nature, is particularly attractive for various wireless applications, especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G. In this article, we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We then describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d5318a4ebb4160743e3af568b310ebc5aaebc09e

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Citations in the corpus

Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training With Non-IID Private Data (2020, background): “FL [1], [2], [3], [4], [12] is a distributed”
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, methodology): “The rationale is to collaboratively train a shared model such that participating devices train the local models and only share the updates (instead of data) with the centralized parameter server [15], [212].”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “Moreover, the authors of [91] described the FL implementation challenges, methods, and future directions in 6G communications.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “The integration of FL and 6G wireless applications was explored in [20], where the key challenges of using FL for 6G networks are highlighted.”
Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges (2021, background): “In this way, distributed learning and federated learning [220], which can coordinate the learning process on millions of distributed devices (local FL models) to improve the quality of the centralized”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “implement edge artificial intelligence (AI) in B5G networks [8], [9], [10], [11], [12], [13].”
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges (2019, background): “Neural network structures [257] and federated learning technologies [258] being discussed for the”
Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas (2022, background): “Apart from the soft skills required, gaining technical skills is also an issue for human workers [2, 101].”
Federated Learning for Malware Detection in IoT Devices (2021, background): “Despite the novelty and benefits of FL approaches, their application in real-world scenarios still presents several open questions that must be analyzed and solved (or at least improved) [9].”
Sionna: An Open-Source Library for Next-Generation Physical Layer Research (2022, background): “Some of the most prominent 6G research topics are communication in the Terahertz band [4], cell-free, holographic, and very large aperture multiple-input multiple-output (MIMO) [5], unmanned aerial vehicle (UAV) and satellite communication [6], machine learning (ML)-based air interface design [7], s”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “such as resource management, network control, interference alignment and user grouping [39].”