Frequently asked questions

Q: How do I remove/replace the compute package?

We do not provide an out-of-the box way to clear the compute package for a model that has been intitialized. This is a security constraint enforced to not allow for arbitrary code package replacement in an already configured federation. However, during development of a new model it will be necessary to reinitialize.

Set a new compute package using the API:

>>> from fedn import APIClient
>>> client = APIClient(host="localhost", port=8092)
>>> client.set_package("package.tgz", helper="numpyhelper")
>>> client.set_initial_model("seed.npz")

Restart the clients.

Q: Can I skip fetching the remote package and instead use a local folder when developing the compute package

Yes, to facilitate interactive development of the compute package you can start a client that uses a local folder ‘client’ in your current working directory by:

fedn client start --remote=False -in client.yaml

Note that in production federations this options should in most cases be disallowed.

Q: How can other aggregation algorithms can be defined?

There is a plugin interface for extending the framework with new aggregators. See

Aggregators

Q: What is needed to include additional ML frameworks in FEDn?

You need to make sure that FEDn knows how to serialize and deserialize the model object. If you can serialize to a list of numpy ndarrays in your compute package entrypoint (see the Quickstart Tutorial code), you can use the built in “numpyhelper”. If this is not possible, you can extend the framework with a custom helper, see the section about model marshaling:

Model Serialization/Deserialization

Q: Can I start a client listening only to training requests or only on validation requests?:

Yes! You can toggle which message streams a client subscibes to when starting the client. For example, to start a pure validation client:

fedn client start --trainer=False -in client.yaml

Q: How do you approach the question of output privacy?

We take security in (federated) machine learning seriously. Federated learning is a foundational technology that impoves input privacy in machine learning by allowing datasets to stay local and private, and not copied to a server. FEDn is designed to provide an industry grade implementation of the core communication and aggregration layers of federated learning, as well as configurable modules for traceability, logging etc, to allow the developer balance between privacy and auditability. With FEDn Studio we add functionality for user authentication, authorization, and federated client identity management. As such, The FEDn Framework provides a comprehensive software suite for implemeting secure federated learning following industry best-practices.

Going beyond input privacy, there are several additional considerations relating to output privacy and potential attacks on (federated) machine learning systems. For an introduction to the topic, see this blog post:

Output Privacy and Federated Machine Learning

Striking the appropriate balance between system complexity and security becomes a use-case dependent endeavor, and we are happy to support projects with guidance on these matters. For an example, one might consider layering differential privacy on top of the aggregation to protect against an honest-but-curious server, at the price of a reduced accuracy for the global model. Depending on the privacy requirements, the model type, the amount of data, the number of local updates possible during training etc, this may or may not be necessary.

We are engaged in several cybersecurity projects focused on federated machine learning, do not hesitate to reach out to discuss further with the Scaleout team.