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A Review of Client Scheduling Strategies in Federated Learning

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Advances in Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1587))

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Abstract

Federated learning is a distributed machine learning method to solve the problems of ‘data islands’ and privacy protection. Now it has become one of the research hotspots in the field of learning. Like many learning methods, Federated Learning is a data-driven learning framework. When facing the challenges of devices heterogeneity, non identically and independently distributed (Non-IID) data and data’s security, it could not be processed simply and abstractly like other learning paradigms. This paper briefly introduces the definition of Federated Learning and the challenges faced by all parties, and mainly summarizes the client scheduling strategies in Federated Learning in recent years. Client scheduling is an important part of the aggregation strategy of Federated Learning. At present, it is very difficult to reduce resource consumption and make the joint model more excellent and personalized. Client scheduling strategy needs to balance the relationship between optimization objectives and task objectives. The summary will help us understand the development situation of the current field and provide a clear direction for future research.

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Acknowledgement

We thank anonymous reviewers for their helpful comments in improving the paper. This work is supported in part by Key-Area Research and Development Program of Guangdong Province (No. 2019B010137005).

Funding

Key-Area Research and Development Program of Guangdong Province No. 2019B010137005.

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Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510000, China

    Zhikai Yang, Yaping Liu, Shuo Zhang, Xingyu Lv & Fangyu Shen

  2. Peng Cheng Laboratory Shenzhen, Shenzhen, 518000, China

    Yaping Liu & Shuo Zhang

Authors
  1. Zhikai Yang
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  2. Yaping Liu
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  3. Shuo Zhang
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  4. Xingyu Lv
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  5. Fangyu Shen
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Corresponding author

Correspondence to Yaping Liu .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Yang, Z., Liu, Y., Zhang, S., Lv, X., Shen, F. (2022). A Review of Client Scheduling Strategies in Federated Learning. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1587. Springer, Cham. https://doi.org/10.1007/978-3-031-06761-7_15

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  • DOI: https://doi.org/10.1007/978-3-031-06761-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06760-0

  • Online ISBN: 978-3-031-06761-7

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