Federated Learning (FL) presents a promising approach to machine learning (ML) by allowing multiple sources of data (devices or entities) to collaboratively train a shared model while keeping data decentralised. This approach mitigates privacy risks as raw data remains locally on the sources,...

Better implementation of EU policies and programmes could save EU taxpayers and businesses billions of euros every year. Insufficient administrative capacity can lead to problems in implementation, especially when necessary competences - knowledge, skills and attitudes - are inadequate or...

Federated Learning (FL) is an innovative technique that allows multiple firms to collaborate in training machine learning models while preserving data privacy. This is especially important in industries where data is sensitive or subject to regulations like the General Data Protection Regulation...