@misc{https://doi.org/10.48550/arxiv.2202.12443, author = {Baracaldo, Nathalie and Anwar, Ali and Purcell, Mark and Rawat, Ambrish and Sinn, Mathieu and Altakrouri, Bashar and Balta, Dian and Sellami, Mahdi and Kuhn, Peter and Buchinger, Matthias}, title = {Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach}, publisher = {arXiv}, year = {2022}, month = feb, owner = {arXiv.org perpetual, non-exclusive license}, abstract = {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.}, doi = {10.48550/ARXIV.2202.12443}, keywords = {Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, url = {https://arxiv.org/abs/2202.12443}, }