mlsanityData to 14 Oct 2026

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation

arXiv 2205.11518 ↗First posted 23 May 2022cs.CR cs.AI cs.LGcited by 3

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Abstract (arXiv)

In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new influence approximation called "lazy influence" to filter and score data while preserving privacy. To do this, each participant uses their own data to estimate the influence of another participant's batch and sends a differentially private obfuscated score to the central coordinator. Our method has been shown to successfully filter out biased and corrupted data in various simulated and real-world settings, achieving a recall rate of over >90% (sometimes up to 100%) while maintaining strong differential privacy guarantees with varepsilon ≤ 1.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 37 of its 63 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

37 of 63 references.

References · 37

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  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
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25 more
  1. 2019Federated Machine Learning: Concept and Applicationscited by 412
  2. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  3. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 383
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  17. 2020Byzantine-Resilient Secure Federated Learningcited by 93
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  20. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  21. 2017Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12cited by 43
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  23. 2020Toward Understanding the Influence of Individual Clients in Federated Learningcited by 16
  24. 2020Evaluating and Rewarding Teamwork Using Cooperative Game Abstractionscited by 4
  25. 2020A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settingscited by 1

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Stochastic Self-Organization in Multi-Agent Systemscited by 9
  2. 2025Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Datacited by 0
  3. 2025CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learningcited by 0