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.

With the continuous rolling-out of edge computing, Federated Learning (FL) has become a promising solution for intelligent Internet-of-things (IoT). In addition to resource constraints, deploying FL schemes in IoT networks is greatly challenged byheterogeneityin multiple dimensions. While heterogeneity in data distribution and computation capability has been extensively studied, the impact of distinct, even hybrid training paradigms on FL performances remains largely unknown. To answer this open question in the IoT context, we propose aHybrid-Training Federated Learning(HT-FL) algorithm for the power-constrained IoT networks, incorporating both sequential and parallel training that naturally adapts to various sub-network topologies, while greatly reducing the energy consumption during the training stage. We demonstrate through analysis that the convergence of HT-FL is theoretically guaranteed, achieving$O (\frac{1}{\sqrt{K}})$for carefully chosen learning rates. Experiments on multiple datasets show that, the proposed HT-FL outperforms existing FL schemes on multiple training tasks under various data distribution settings, while reducing an average of 20% energy consumption. In a more practical sense, a self-adaptive parameter-tuning strategy is also designed for HT-FL deployment, which can be easily extended to other multi-layer FL schemes in complex application scenarios.