Federated and decentralized learning have become key building blocks for privacy-preserving machine learning. Participation in these opaque federations may be better incentivized by transparent communication of each user's contribution. For real-world applications with large numbers of heterogeneous participants, quantifying these contributions according to their impact on model quality remains challenging. We discuss the applicability various contribution measures with a particular focus on the personalized learning setting, where each participant has their own learning objective.