实证发现现有协作学习研究难解跨组织联邦学习落地难题
Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
- 通过多方访谈揭示跨组织联邦学习真实挑战
- 发现模型性能、激励机制与信任问题成主要障碍
- 为实际应用提供研究方向，适合关注落地的从业者
跨组织联邦学习（Cross-silo FL）是一种在不直接共享私有数据的前提下，实现跨机构机器学习协作的有前景方法。尽管受GDPR、HIPAA等数据保护法规推动，组织兴趣高涨，但其实际应用仍有限。本文通过访谈用户机构、软件提供商及学术研究人员等多元利益相关者，揭示了跨组织联邦学习在实践中面临的一系列挑战，包括对模型性能的担忧、合作方间激励与信任问题。研究显示，这些挑战尚未被现有研究充分捕捉，且与跨设备联邦学习有显著差异。文章最后讨论了未来研究方向，以助力克服这些现实障碍。
原文摘要 · Abstract (English)
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we conduct an interview study to understand the practical challenges associated with cross-silo FL adoption. With interviews spanning a diverse set of stakeholders such as user organizations, software providers, and academic researchers, we uncover various barriers, from concerns about model performance to questions of incentives and trust between participating organizations. Our study shows that cross-silo FL faces a set of challenges that have yet to be well-captured by existing research in the area and are quite distinct from other forms of federated learning such as cross-device FL. We end with a discussion on future research directions that can help overcome these challenges.
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