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It has been shown that the input data can be reverse-engineered from the model weights. How do you deal with this issue?
looks like they've introduced some differential privacy wrappers, the changelog points to that: https://github.com/adap/flower/blob/94a1f942abfce5dff4e9aff2...
Earlier quoted context omitted.
Thanks for the question, very natural to ask. We are also fans of PySft. It offers support for a very wide range of privacy enhancing machine learning tools. But where Flower and PySft differ is in focus. Federated learning is difficult and requires many technical moving parts all working together (e.g., secure aggregation, differential privacy, scalable simulation, device deployments, integration with conventional M…
Can confirm that PySyft is currently in the process of integrating with Flower. Best of both worlds.
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As a founder of an FL startup, I strongly advise against using FedML as it is one of the worst frameworks in terms of scalability. Regrettably, the FedML team has also a poor reputation due to their toxicity and suspicious behavior. If you value the trustworthiness of your product, I suggest avoiding FedML at all costs.
Have you had any luck convincing hospitals / insurers / etc that this satisfies HIPAA and is safe? How do you convince them?
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This is really cool. Federated learning seems like it could unlock a lot of value in healthcare settings. Have you had any luck convincing hospitals / insurers / etc that this satisfies HIPAA and is safe? How do you convince them?
Others asked similar question regarding comparable projects. What's your take on OpenFL from Intel? Do you think Flower moves into more commercial-MLOps direction? Looks like OpenFL particularly focused on to academic imaging community.