DISCO让非技术用户零数据共享协作训练模型，保护隐私且无需编程。
DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning
- 在浏览器中本地训练，无需上传原始数据
- 支持联邦与去中心化模式，可选多种隐私保护策略
- 适合教育、医疗等数据敏感领域，无编程基础者也可用
数据因隐私、知识产权和法律限制难以共享，不仅削弱了预测模型的统计效能，还造成资源不均导致的准确性偏差。我们提出 DISCO：一个开源的分布式协同学习平台，面向非技术用户，可在不共享原始数据、无需编程知识的前提下协作构建机器学习模型。DISCO 的网页应用直接在浏览器中本地训练模型，实现跨平台开箱即用，包括智能手机。其模块化设计支持联邦与去中心化范式，提供不同等级的隐私保障及多种权重聚合策略，可实现模型个性化并增强对偏见的鲁棒性。代码仓库位于 https://github.com/epfml/disco，演示界面为 https://discolab.ai。
原文摘要 · Abstract (English)
Data is often impractical to share for a range of well considered reasons, such as concerns over privacy, intellectual property, and legal constraints. This not only fragments the statistical power of predictive models, but creates an accessibility bias, where accuracy becomes inequitably distributed to those who have the resources to overcome these concerns. We present DISCO: an open-source DIStributed COllaborative learning platform accessible to non-technical users, offering a means to collaboratively build machine learning models without sharing any original data or requiring any programming knowledge. DISCO's web application trains models locally directly in the browser, making our tool cross-platform out-of-the-box, including smartphones. The modular design of \disco offers choices between federated and decentralized paradigms, various levels of privacy guarantees and several approaches to weight aggregation strategies that allow for model personalization and bias resilience in the collaborative training. Code repository is available at https://github.com/epfml/disco and a showcase web interface at https://discolab.ai
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