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<p>Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets is an efficient way to address both these issues in centralized learning environments, it is challenging to detect and address these issues in a distributed learning environment such as federated learning. In this paper, we propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning. At the heart of our contribution lies the popular Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme, which is used by the clients to privately share their data attributes, and subsequently balance their datasets before implementing the federated learning scheme. Extensive experimental results show that our proposed method improves the federated learning accuracy numbers by up to 8<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\%\)</EquationSource> </InlineEquation> when used along with popular datasets and relevant baselines.</p>

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On homomorphic encryption based strategies for class imbalance in federated learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月28日

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  • Arpit Guleria,
  • Harshan Jagadeesh,
  • Ranjitha Prasad,
  • B. N. Bharath

摘要

Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets is an efficient way to address both these issues in centralized learning environments, it is challenging to detect and address these issues in a distributed learning environment such as federated learning. In this paper, we propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning. At the heart of our contribution lies the popular Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme, which is used by the clients to privately share their data attributes, and subsequently balance their datasets before implementing the federated learning scheme. Extensive experimental results show that our proposed method improves the federated learning accuracy numbers by up to 8 \(\%\) when used along with popular datasets and relevant baselines.