Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights

• URL: http://arxiv.org/abs/2312.04688v1
• Date: Thu, 7 Dec 2023 20:39:57 GMT
• Title: Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
• Authors: Maryam Ben Driss, Essaid Sabir, Halima Elbiaze, Walid Saad
• Abstract summary: This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack. It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
• Score: 52.024964564408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of wireless systems, such as sixth-generation (6G) mobile network. However, massive data, energy consumption, training complexity, and sensitive data protection in wireless systems are all crucial challenges that must be addressed for training AI models and gathering intelligence and knowledge from distributed devices. Federated Learning (FL) is a recent framework that has emerged as a promising approach for multiple learning agents to build an accurate and robust machine learning models without sharing raw data. By allowing mobile handsets and devices to collaboratively learn a global model without explicit sharing of training data, FL exhibits high privacy and efficient spectrum utilization. While there are a lot of survey papers exploring FL paradigms and usability in 6G privacy, none of them has clearly addressed how FL can be used to improve the protocol stack and wireless operations. The main goal of this survey is to provide a comprehensive overview on FL usability to enhance mobile services and enable smart ecosystems to support novel use-cases. This paper examines the added-value of implementing FL throughout all levels of the protocol stack. Furthermore, it presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry and sustain the development of cutting-edge mobile services.

Related papers

• Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework [1.4206132527980742]
  Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. We present the recent advances in developing APPFL, a framework and benchmarking suite for federated learning. We demonstrate the capabilities of APPFL through extensive experiments evaluating various aspects of FL, including communication efficiency, privacy preservation, computational performance, and resource utilization.
  arXiv  Detail & Related papers  (2024-09-17T22:20:26Z)
• FLEX: FLEXible Federated Learning Framework [6.112199274064954]
  This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques.
  arXiv  Detail & Related papers  (2024-04-09T08:51:05Z)
• The Role of Federated Learning in a Wireless World with Foundation Models [59.8129893837421]
  Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. Currently, the exploration of the interplay between FMs and federated learning (FL) is still in its nascent stage. This article explores the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities.
  arXiv  Detail & Related papers  (2023-10-06T04:13:10Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges [14.008159759350264]
  A new technique, coined as federated learning (FL), arose to bring machine learning to the edge of wireless networks. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies.
  arXiv  Detail & Related papers  (2021-11-14T17:13:34Z)
• Fusion of Federated Learning and Industrial Internet of Things: A Survey [4.810675235074399]
  Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. In order to address this issue, federated learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models.
  arXiv  Detail & Related papers  (2021-01-04T06:28:32Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)
• Wireless Communications for Collaborative Federated Learning [160.82696473996566]
  Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models. Google's seminal FL algorithm requires all devices to be directly connected with a central controller. This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
  arXiv  Detail & Related papers  (2020-06-03T20:00:02Z)
• Evaluating the Communication Efficiency in Federated Learning Algorithms [3.713348568329249]
  Recently, in light of new privacy legislations in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models, without sharing the privacy-sensitive data. This raises the challenge of communication cost when implementing FL at large scale.
  arXiv  Detail & Related papers  (2020-04-06T15:31:54Z)

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.