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Privacy-Preservation for Federated Learning: Survey and Future Directions

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Computing, Communication and Learning (CoCoLe 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2317))

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  • 14 Accesses

Abstract

Federated learning (FL) is a decentralized machine learning approach where devices train models on their data without directly sharing it. This ensures privacy by transmitting only encrypted updates of the model to a central server for aggregation. FL offers advantages like stronger privacy, lower communication costs, and working with diverse data sources. However, FL can still be vulnerable to attacks compromising privacy, disrupting model performance, or weakening system security. Researchers are developing methods to address these security and privacy concerns. This survey explores current FL techniques and known security and privacy issues with their mitigation strategies. Finally, it highlights the challenges and future directions for ensuring user privacy and model effectiveness, which is essential for the broader adoption of FL.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Technology Nirma University, Ahmedabad, 382481, Gujarat, India

    Deepti Saraswat, Tushar Mali, Anand Verma & Sunil Gautam

Authors
  1. Deepti Saraswat
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  2. Tushar Mali
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  3. Anand Verma
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  4. Sunil Gautam
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Corresponding author

Correspondence to Sunil Gautam .

Editor information

Editors and Affiliations

  1. National Institute of Technology, Warangal, Telangana, India

    Sanjaya Kumar Panda

  2. The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  3. National Institute of Technology, Warangal, Telangana, India

    Rashmi Ranjan Rout

  4. National Institute of Technology, Warangal, Telangana, India

    Manjubala Bisi

  5. National Institute of Technology, Warangal, Telangana, India

    Sangharatna Godboley

  6. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  7. Indian Statistical Institute, Kolkata, West Bengal, India

    Ashish Ghosh

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Saraswat, D., Mali, T., Verma, A., Gautam, S. (2025). Privacy-Preservation for Federated Learning: Survey and Future Directions. In: Panda, S.K., et al. Computing, Communication and Learning. CoCoLe 2024. Communications in Computer and Information Science, vol 2317. Springer, Cham. https://doi.org/10.1007/978-3-031-79041-6_25

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  • DOI: https://doi.org/10.1007/978-3-031-79041-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-79040-9

  • Online ISBN: 978-3-031-79041-6

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