Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges

2019/07/30 by Solmaz Niknam, Harpreet S. Dhillon, Niknam, Solmaz +3 · 12 citations
Computer Science · Engineering · Mathematics · #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML

paper · pdf · doi:10.48550/arxiv.1908.06847

openalex publication_date 2019/07/30 · arxiv created 2020/05/03 · arxiv updated 2020/05/05 · openalex created_date 2023/02/19 · openalex updated_date 2026/07/28

Abstract

There is a growing interest in the wireless communications community to complement the traditional model-based design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Owing to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.

Cited by

• Age-of-Information Aware Federated Learning with Finite Speed Pinching Antenna
• FedMPDD: Communication-Efficient Federated Learning with Privacy Preservation Attributes via Projected Directional Derivative
• On the Optimization of Model Aggregation for Federated Learning at the Network Edge
• Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge
• Rethinking Federated Learning Over the Air: The Blessing of Scaling Up
• Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
• From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
• On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins
• Federated Learning in Active STARS-Aided Uplink Networks
• Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems
• A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication
• Federated Learning for Cyber Physical Systems: A Comprehensive Survey

Related

• Federated learning and next generation wireless communications: A survey on bidirectional relationship
• Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends
• Federated Learning for 6G: Applications, Challenges, and Opportunities
• Federated Learning and Wireless Communications
• Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges