DFFL: Federated Learning with Data Fairness

Federated Learning (FL) is a promising solution for distributed cooperative training in various scenarios. FL empowers data-rich devices to contribute to a comprehensive model, but it suffers from the data fairness problem, where local datasets are not fairly accounted for in the global model. This problem is especially devastating in the scenario of heterogeneous devices with non-lID data distributions. To resolve this issue, we propose Federated Learning with Data Fairness (DFFL), which incorporates model factorization to adapt local models of different sizes to devices of various computation capabilities, and a novel DNM metric for client selection to boost its performance on devices with minor data distributions. Experiments show that, the proposed DFFL outperforms existing FL schemes, achieving higher accuracy with much lower (82 % less local parameters on average) computation load, while providing a more robust assurance of data fairness among devices.