![]() ![]() At the end you'll find a dockerfile capturing all mentioned steps. To reduce complexity, we focus therefore on the latest version of syft, at the time of writing this post. This is especially the case for older PySyft versions like v0.2.9 and v0.3.0. The CPU architecture is ARM based and some Python packages are not instantly available over pip install for example. It is a Single-Board-Computer (SBC), which can handle data acquisition and control, data processing and storage, connectivity and power management. A Raspberry Pi can be a good choice for simulating the data owner’s device. Running experiments with the data scientist and data owner being on separate devices is important to account for possible hardware constraints. įederated Learning systems can either be tested with virtual nodes on the same machine or with physically separated nodes. Some examples of such regulations are the General Data Protection Regulation in the European Union, the California Consumer Privacy Act in the USA or the Personal Data Protection (Amendment) Act in Singapore. This approach is especially interesting in the context of high demand for big data to train AI models on the one side and data privacy regulations on the other side. ![]() This method is part of privacy preserving machine learning and allows data scientists to work with remote data, without revealing it. The PySyft framework enables practitioners and stakeholders in the AI domain to leverage the potential of Federated Learning. ![]()
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