  Federated learning (FL) is a novel collaborative machine learning framework
designed to preserve privacy while enabling the creation of robust models. This
paradigm addresses a growing need for data security by allowing multiple
participants to contribute to a model without exposing their individual
datasets. A pivotal issue within this framework, however, concerns the fair and
accurate attribution of contributions from various participants to the creation
of the joint global model. Incorrect contribution distribution can erode trust
among participants, result in inequitable compensation, and ultimately diminish
the willingness of parties to engage or actively contribute to the federation.
While several methods for remunerating participants have been proposed, little
attention was given to the analysis of the stability of these methods when
evaluating contributions, which is critical to ensure the long-term viability
and fairness of FL systems. In this paper, we analyse this stability through
the calculation of contributions by gradient-based model reconstruction
techniques with Shapley values. Our investigation reveals that Shapley values
fail to reflect baseline contributions, especially when employing different
aggregation techniques. To address this issue, we extend on established
aggregation techniques by introducing FedRandom, which is designed to sample
contributions in a more equitable and distributed manner. We demonstrate that
this approach not only serves as a viable aggregation technique but also
significantly improves the accuracy of contribution assessment compared to
traditional methods. Our results suggest that FedRandom enhances the overall
fairness and stability of the federated learning system, making it a superior
choice for federations with limited number of participants.
