# A Fairness-Aware Peer-to-Peer Decentralized Learning Framework with Heterogeneous Devices
## 6 Discussion
### Federated Learning Heterogeneity Issues  
With the advance and deployment of IoT systems, distributed machine learning has attracted great attention [39]. Federated learning, one of the representative distributed machine learning technologies, tends to train a global learning model with a group of local clients. Federated learning adopts a central parameter to organize the training process, including the model aggregation and model update. To eliminate the privacy concern, none of the nodes are allowed to access other local datasets. On the other hand, federated applications, such as the G-board, show their great potential to embrace distributed IoT devices [40].  
However, the inherent heterogeneity of IoT devices becomes the emerging bottleneck to deploy the federated learning framework under a large-scale real-world scenario. The inherent heterogeneity of IoT devices could be cataloged as three aspects, which include non-i.i.d. data distribution, unbalanced data, and heterogeneous devices. In this stage, there are some research efforts focusing on training a global learning model with non-i.i.d. local clients. For example, [PERSON] et al. [21] investigated the learning performance  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{Numerical results in Case 1 (CIFAR-10)} \\ \hline  & **LA below 55\%** & **LA over 55\%** & **LA over 65\%** \\ \hline
**Our Approach** & 3 & 17 & 10 \\ \hline
**DL-Avg** & 5 & 15 & 6 \\ \hline
**DFL-Seg-Avg** & 1 & 19 & 9 \\ \hline  & \multicolumn{3}{c}{Numerical results in Case 2 (CIFAR-10)} \\ \hline  & **LA below 55\%** & **LA over 55\%** & **LA over 65\%** \\ \hline
**Our Approach** & 4 & 16 & 5 \\ \hline
**DL-Avg** & 6 & 14 & 4 \\ \hline
**DFL-Seg-Avg** & 4 & 16 & 6 \\ \hline  & \multicolumn{3}{c}{Numerical results in Case 3 (CIFAR-10)} \\ \hline  & **LA below 55\%** & **LA over 55\%** & **LA over 65\%** \\ \hline
**Our Approach** & **5** & **15** & 6 \\ \hline
**DL-Avg** & 15 & 5 & 0 \\ \hline
**DFL-Seg-Avg** & 10 & 10 & 0 \\ \hline  & \multicolumn{3}{c}{Numerical results in Case 4 (CIFAR-10)} \\ \hline  & **LA below 55\%** & **LA over 55\%** & **LA over 65\%** \\ \hline
**Our Approach** & **12** & **8** & **5** \\ \hline
**DL-Avg** & 14 & 6 & 2 \\ \hline
**DFL-Seg-Avg** & 18 & 2 & 1 \\ \hline  & \multicolumn{3}{c}{LA = (Local Accuracy)} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of nodes in various accuracy distributions (CIFAR-10).  
of federated learning under non-i.i.d. data distribution. Authors first showed that the accuracy of federated learning degrades significantly with highly skewed non-i.i.d. data. Moreover, authors analyzed this accuracy reduction that could be evaluated via the earth mover's distance (EMD).  
We consider unbalanced data and heterogeneous devices to still lead unsolved challenges. Unbalanced data cause the learning fairness in federated learning. For example, the global learning would be dominated by the local clients with a massive dataset, ignoring the learning objectives of the relatively small nodes. Heterogeneous IoT devices bring the extravagant communication cost into federated learning. This is because the lack of a compatible communication protocol confines the deployment of federated learning over a real-world scenario. Thus, the design of compatible communication protocols requires further research.