Contribution Evaluation in Federated Learning: Examining Current
  Approaches
Vasilis Siomos Jonathan Passerat-Palmbach
Federated Learning (FL) has seen increasing interest in cases where entities
want to collaboratively train models while maintaining privacy and governance
over their data. In FL, clients with private and potentially heterogeneous data
and compute resources come together to train a common model without raw data
ever leaving their locale. Instead, the participants contribute by sharing
local model updates, which, naturally, differ in quality. Quantitatively
evaluating the worth of these contributions is termed the Contribution
Evaluation (CE) problem. We review current CE approaches from the underlying
mathematical framework to efficiently calculate a fair value for each client.
Furthermore, we benchmark some of the most promising state-of-the-art
approaches, along with a new one we introduce, on MNIST and CIFAR-10, to
showcase their differences. Designing a fair and efficient CE method, while a
small part of the overall FL system design, is tantamount to the mainstream
adoption of FL.