In Q1 we focus entirely on using XayNet for the Xayn app in terms of federated learning and first simple analytics, such as gathering relevant AI performance data like NDCG metrics because we want to know how our AI models perform without violating the privacy of our users. As you know, our framework originated with the aim to aggregate machine learning models securely and privately between edge devices. Thereby, the models are transformed into one-dimensional lists so that at the end we only aggregate a list of numbers, so why not also aggregate other numerical analytics data, like AI performance metrics or user behaviour, such as screen times in our app, all of course with the privacy guarantees of XayNet. As such, we focus predominantly on mobile cross-device learning but also extend our framework to cover such use cases. In Q1 we take however mostly care about the internal mobile case and testing so we set the basis to further generalisation to external cases in the community during the rest of the year.
In Q2 we have three main focus points: Extending XayNet to support also web applications, since also our Xayn app will be provided as a web version via WASM; integrating our product analytics extensions in our Xayn app and optimising the client for higher performance, which is one the major bottlenecks.
In Q3, we can imagine to opening up the analytics layer also to more general use cases outside of Xayn itself. Until then our core focus is predominantly internally, yet, of course we hope to get community and external feature suggestions and reviews. Also we want to make the coordinator more observable as a foundation for further optimisations.