Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

• URL: http://arxiv.org/abs/2110.04160v1
• Date: Fri, 8 Oct 2021 14:36:43 GMT
• Title: Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
• Authors: Thippa Reddy Gadekallu, Quoc-Viet Pham, Thien Huynh-The, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, and Madhusanka Liyanage
• Abstract summary: We present a survey on the use of federated learning for big data services and applications. We review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation.
• Score: 5.124701758921822
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices. The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. However, this conventional approach faces critical issues in terms of data privacy as the data may include sensitive data such as personal information, governments, banking accounts. To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. However, a gap exists in the literature that a comprehensive survey on FL for big data services and applications is yet to be conducted. In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data. In particular, we extensively review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation. Subsequently, we review the potential of FL for big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media. Further, we summarize a number of important projects on FL-big data and discuss key challenges of this interesting topic along with several promising solutions and directions.

Related papers

• Federated Large Language Models: Current Progress and Future Directions [63.68614548512534]
  This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
  arXiv  Detail & Related papers  (2024-09-24T04:14:33Z)
• Data-Centric AI in the Age of Large Language Models [51.20451986068925]
  This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs) We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
  arXiv  Detail & Related papers  (2024-06-20T16:34:07Z)
• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights [52.024964564408]
  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.
  arXiv  Detail & Related papers  (2023-12-07T20:39:57Z)
• Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications [6.042202852003457]
  Federated learning (FL) is a technique for developing robust machine learning (ML) models. To protect user privacy, FL requires users to send model updates rather than transmitting large quantities of raw and potentially confidential data. This survey provides a comprehensive analysis and comparison of the most recent FL algorithms.
  arXiv  Detail & Related papers  (2023-10-08T19:54:26Z)
• LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting [65.71129509623587]
  Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning. However, the promising results achieved on current public datasets may not be applicable to practical scenarios. We introduce the LargeST benchmark dataset, which includes a total of 8,600 sensors in California with a 5-year time coverage.
  arXiv  Detail & Related papers  (2023-06-14T05:48:36Z)
• Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria [0.0]
  The term Big Data has been coined to refer to the gargantuan bulk of data that cannot be dealt with by traditional data-handling techniques. This paper explores the attributes of big data that are relevant to educational institutions. It investigates the factors influencing the adoption of big data and analytics in learning institutions.
  arXiv  Detail & Related papers  (2022-07-29T13:52:24Z)
• DataPerf: Benchmarks for Data-Centric AI Development [81.03754002516862]
  DataPerf is a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We provide an open, online platform with multiple rounds of challenges to support this iterative development. The benchmarks, online evaluation platform, and baseline implementations are open source.
  arXiv  Detail & Related papers  (2022-07-20T17:47:54Z)
• Towards Federated Long-Tailed Learning [76.50892783088702]
  Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of learning from pervasive private data, and on the other side, learn from long-tailed data. This paper focuses on learning with long-tailed (LT) data distributions under the context of the popular privacy-preserved federated learning (FL) framework.
  arXiv  Detail & Related papers  (2022-06-30T02:34:22Z)
• "If we didn't solve small data in the past, how can we solve Big Data today?" [0.0]
  We aim to research terms such as'small' and 'big' data, understand their attributes, and look at ways in which they can add value. Based on the research, it can be inferred that, regardless of how small data might have been used, organizations can still leverage big data with the right technology and business vision.
  arXiv  Detail & Related papers  (2021-11-08T16:31:01Z)
• DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities [0.11470070927586015]
  Federated learning (FL) emerges as a functional solution to build high-performance models shared among multiple parties. We present DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. We describe particularly the scenario where our DID-eFed enables the FL among hospitals and research institutions.
  arXiv  Detail & Related papers  (2021-05-18T16:55:34Z)
• 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.