mlsanityData to 14 Oct 2026

Gradients Stand-in for Defending Deep Leakage in Federated Learning

arXiv 2410.08734 ↗First posted 11 Oct 2024cs.LG cs.CVcited by 2

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Abstract (arXiv)

Federated Learning (FL) has become a cornerstone of privacy protection, shifting the paradigm towards localizing sensitive data while only sending model gradients to a central server. This strategy is designed to reinforce privacy protections and minimize the vulnerabilities inherent in centralized data storage systems. Despite its innovative approach, recent empirical studies have highlighted potential weaknesses in FL, notably regarding the exchange of gradients. In response, this study introduces a novel, efficacious method aimed at safeguarding against gradient leakage, namely, ``AdaDefense". Following the idea that model convergence can be achieved by using different types of optimization methods, we suggest using a local stand-in rather than the actual local gradient for global gradient aggregation on the central server. This proposed approach not only effectively prevents gradient leakage, but also ensures that the overall performance of the model remains largely unaffected. Delving into the theoretical dimensions, we explore how gradients may inadvertently leak private information and present a theoretical framework supporting the efficacy of our proposed method. Extensive empirical tests, supported by popular benchmark experiments, validate that our approach maintains model integrity and is robust against gradient leakage, marking an important step in our pursuit of safe and efficient FL.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 53 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

35 of 53 references.

References · 35

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23 more
  1. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentialscited by 0
  2. 2026DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphscited by 0