On top of the building blocks specified by the DSSC, the following building blocks are considered to be essential for the Skills Data Space.
7.6.1. Decentralised AI training #
By mobilising data at its source, decentralised learning increases the relevance of AIs by allowing them to train on more transversal, sensitive and up-to-date data, from multiple sources: data is no longer shared to benefit from the service.
|Key Functions in skills context||For the skills context and its specific requirements in terms of AI applications, specific functions are required:
· AI providers need user data to train their models, while data providers need AI models to provide innovative features to their users. This building block is an answer to this need by making the link between AI providers and data providers through secure and trusted decentralized learning protocol.
· Users give or revoke consent or their personal data through intuitive UI, translating into semantic description.
· Once a user gives consent to participate in an AI model,
· Each contributor securely receives the AI model to train, including the weights, and the related algorithms. The relevant user data, already in place, is identified and queried.
· The computation is then made in a secure environment to guarantee the robustness and trustworthiness of the execution.
· Once the contribution is computed, the result is split in shares and a noise is added to each share to ensure the confidentiality of the contribution. The noise is computed in such a way that at the end of the execution, the aggregation of all the contributions removes the overall noise and produces the final trained model, which can be retrieved by the AI provider.
· During the process, no user data is exposed whatsoever, ensuring the security and the privacy of the users.
· Any organisation or individual can implement its own learning model and submit it to a call for contribution so that individuals can participate with their data.
· Anonymised & High-Quality Statistical Data are usable:
· AI explainability and interpretation: Explainability is a key requirement for trust. Federated learning has not yet been adequately studied in the context of inherently explainable models. Once the global model is computed, this block will provide a reliable approach that provides clear and understandable explanations of the model outputs.
· Handle data heterogeneity: This block quantifies user heterogeneity in terms of quantity, quality, and distribution, as well as its impact on the overall model. A notion of score is to be defined to quantify the clients’ contributions to the overall model and provide a benefit to clients who contribute positively.
|Dependencies and relationships|
|Commonly Used Standards in skills context||No standards found in DS4Skills inventory|
|Specs & Reference Implementations in skills context||No specifications and reference implementations found in DS4Skills inventory|
Table 42: Decentralised AI training.