GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation
  in Federated Learning
  Federated Learning (FL) bridges the gap between collaborative machine
learning and preserving data privacy. To sustain the long-term operation of an
FL ecosystem, it is important to attract high quality data owners with
appropriate incentive schemes. As an important building block of such incentive
schemes, it is essential to fairly evaluate participants' contribution to the
performance of the final FL model without exposing their private data. Shapley
Value (SV)-based techniques have been widely adopted to provide fair evaluation
of FL participant contributions. However, existing approaches incur significant
computation costs, making them difficult to apply in practice. In this paper,
we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to
address this challenge. It reconstructs FL models from gradient updates for SV
calculation instead of repeatedly training with different combinations of FL
participants. In addition, we design a guided Monte Carlo sampling approach
combined with within-round and between-round truncation to further reduce the
number of model reconstructions and evaluations required, through extensive
experiments under diverse realistic data distribution settings. The results
demonstrate that GTG-Shapley can closely approximate actual Shapley values,
while significantly increasing computational efficiency compared to the state
of the art, especially under non-i.i.d. settings.
