# Owen Sampling Accelerates Contribution Estimation in Federated Learning

## TL;DR

This paper introduces FedOwen, a novel federated learning framework that employs Owen sampling to efficiently approximate client contributions, leading to faster convergence and higher accuracy.

## Contribution

FedOwen is the first method to combine Owen sampling with adaptive client selection for efficient contribution estimation in federated learning.

## Key findings

- Achieves up to 23% higher final accuracy
- Maintains small approximation error with same evaluation budget
- Reduces bias and uncovers informative rare data

## Abstract

Federated Learning (FL) aggregates information from multiple clients to train a shared global model without exposing raw data. Accurately estimating each client's contribution is essential not just for fair rewards, but for selecting the most useful clients so the global model converges faster. The Shapley value is a principled choice, yet exact computation scales exponentially with the number of clients, making it infeasible for large federations. We propose FedOwen, an efficient framework that uses Owen sampling to approximate Shapley values under the same total evaluation budget as existing methods while keeping the approximation error small. In addition, FedOwen uses an adaptive client selection strategy that balances exploiting high-value clients with exploring under-sampled ones, reducing bias and uncovering rare but informative data. Under a fixed valuation cost, FedOwen achieves up to 23 percent higher final accuracy within the same number of communication rounds compared to state-of-the-art baselines on non-IID benchmarks.