Feb 25, 2022 · We build on AI FactSheets for instilling transparency and trustworthiness into the AI lifecycle and expand it to incorporate dynamic and nested ...
Feb 25, 2022 · In traditional ML, FactSheets [12] have been proposed as a way to enable such accountability by recording “facts” related to the overall ML.
This work introduces AF^2 Framework, where an auditor can validate, reproduce and certify a FL process, and instrument FL with accountability by fusing ...
Towards an Accountable and Reproducible Federated Learning
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Sep 12, 2024 · We build on AI FactSheets for instilling transparency and trustworthiness into the AI lifecycle and expand it to incorporate dynamic and nested ...
To enable accountability, an auditor can validate, reproduce and certify a federated learning process. The following resources show how our approach works ...
The goal of the Accountable Federated Machine Learning (AFML) project at the Center for AI is to develop a prototype of a city-wide idea classification system.
The FactSheet research project is an effort to foster trust in AI by increasing transparency and enabling governance.
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Towards an accountable and reproducible federated learning: A FactSheets approach. N Baracaldo, A Anwar, M Purcell, A Rawat, M Sinn, B Altakrouri, D Balta ...
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach ... Federated Learning (FL) is an approach to conduct machine learning ...
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach · no code implementations • 25 Feb 2022 • Nathalie Baracaldo, Ali Anwar, ...