This paper proposes a cooperative mechanism for mitigating the performance
degradation due to non-independent-and-identically-distributed (non-IID) data
in collaborative machine learning (ML), namely federated learning (FL), which
trains an ML model using the rich data and computational resources of mobile
clients without gathering their data to central systems. The data of mobile
clients is typically non-IID owing to diversity among mobile clients' interests
and usage, and FL with non-IID data could degrade the model performance.
Therefore, to mitigate the degradation induced by non-IID data, we assume that
a limited number (e.g., less than 1%) of clients allow their data to be
uploaded to a server, and we propose a hybrid learning mechanism referred to as
Hybrid-FL, wherein the server updates the model using the data gathered from
the clients and aggregates the model with the models trained by clients. The
Hybrid-FL solves both client- and data-selection problems via heuristic
algorithms, which try to select the optimal sets of clients who train models
with their own data, clients who upload their data to the server, and data
uploaded to the server. The algorithms increase the number of clients
participating in FL and make more data gather in the server IID, thereby
improving the prediction accuracy of the aggregated model. Evaluations, which
consist of network simulations and ML experiments, demonstrate that the
proposed scheme achieves a 13.5% higher classification accuracy than those of
the previously proposed schemes for the non-IID case.

提出了一种名为 Hybrid-FL 的协作机制，通过启发式算法解决数据和客户端选择问题，从而缓解非独立同分布数据带来的弊端，在网络模拟和机器学习实验中证明了该方案比之前提出的方案在非 IID 情况下具有更高的分类准确率。