论文标题
迈向负责任且可再现的联邦学习：事实表的方法
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
论文作者
论文摘要
联合学习（FL）是基于分散和私人数据的模型共享培训的新型范式。关于道德准则，FL在隐私方面有希望，但需要相对于透明度和可信度。特别是，佛罗里达州必须解决有关当事方的责任及其遵守规则，法律和原则。我们介绍了AF^2框架，在该框架中，我们通过将可验证的索赔与篡改事实融合在一起，以责任为责任。我们基于AI Factsheets将透明度和可信赖性灌输到AI生命周期中，并将其扩展以将动态和嵌套的事实以及FL中的复杂模型组成结合在一起。根据我们的方法，审核员可以验证，复制和证明FL流程。这可以在实践中直接应用，以应对AI工程和道德的挑战。
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data. With respect to ethical guidelines, FL is promising regarding privacy, but needs to excel vis-à-vis transparency and trustworthiness. In particular, FL has to address the accountability of the parties involved and their adherence to rules, law and principles. We introduce AF^2 Framework, where we instrument FL with accountability by fusing verifiable claims with tamper-evident facts, into reproducible arguments. We build on AI FactSheets for instilling transparency and trustworthiness into the AI lifecycle and expand it to incorporate dynamic and nested facts, as well as complex model compositions in FL. Based on our approach, an auditor can validate, reproduce and certify a FL process. This can be directly applied in practice to address the challenges of AI engineering and ethics.
