Scholarlink

<p>Federated learning (FL) is a promising paradigm for collaborative, privacy-preserving training of machine learning (ML) models on decentralized, private data. However, significant challenges limit the broader adoption of FL. First, existing FL workflows are often tailored to specific use cases, requiring extensive manual setup and customization of execution environments. Second, establishing collaborations and matching partners is complicated by the diversity of organizational and collaboration goals, data properties, and data distributions. Third, tracking the provenance of the collaboration process and the artifacts created and used during collaboration is challenging, impeding accountability, transparency, and debugability. Our goal is to address these barriers by lowering the technical expertise required from collaborators. We contribute mechanisms for flexible collaboration composition and creation, automated collaborator matching, and collaboration and artifact provenance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Lowering barriers to federated learning: collaboration management and provenance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月14日

查看全文
  • Marius Schlegel,
  • Daniel Scheliga,
  • Kai-Uwe Sattler,
  • Marco Seeland,
  • Patrick Mäder

摘要

Federated learning (FL) is a promising paradigm for collaborative, privacy-preserving training of machine learning (ML) models on decentralized, private data. However, significant challenges limit the broader adoption of FL. First, existing FL workflows are often tailored to specific use cases, requiring extensive manual setup and customization of execution environments. Second, establishing collaborations and matching partners is complicated by the diversity of organizational and collaboration goals, data properties, and data distributions. Third, tracking the provenance of the collaboration process and the artifacts created and used during collaboration is challenging, impeding accountability, transparency, and debugability. Our goal is to address these barriers by lowering the technical expertise required from collaborators. We contribute mechanisms for flexible collaboration composition and creation, automated collaborator matching, and collaboration and artifact provenance.