  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-\`a-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.
