Federated learning (FL) is a decentralized machine learning approach where independent learners process data privately. Its goal is to create a robust and accurate model by aggregating and retraining local models over multiple rounds. However, FL faces challenges regarding data heterogeneity and model aggregation effectiveness. In order to simulate real-world data, researchers use methods for data partitioning that transform a dataset designated for centralized learning into a group of sub-datasets suitable for distributed machine learning with different data heterogeneity. In this paper, we study the currently popular data partitioning techniques and visualize their main disadvantages: the lack of precision in the data diversity, which leads to unreliable heterogeneity indexes, and the inability to incrementally challenge the FL algorithms. To resolve this problem, we propose a method that leverages entropy and symmetry to construct 'the most challenging' and controllable data distributions with gradual difficulty. We introduce a metric to measure data heterogeneity among the learning agents and a transformation technique that divides any dataset into splits with precise data diversity. Through a comparative study, we demonstrate the superiority of our method over existing FL data partitioning approaches, showcasing its potential to challenge model aggregation algorithms. Experimental results indicate that our approach gradually challenges the FL strategies, and the models trained on FedSym distributions are more distinct.

我们研究了当前流行的数据分区技术，发现它们存在数据多样性不准确和无法挑战联邦学习算法的问题。为了解决这个问题，我们提出了一种使用熵和对称性构建最具挑战性和可控数据分布的方法，并引入了衡量学习代理之间数据异质性的度量。通过比较研究，我们证明了我们的方法优于现有的联邦学习数据分区方法，展示了其挑战模型聚合算法的潜力。实验结果表明，我们的方法逐渐挑战联邦学习策略，并且FedSym分布训练的模型更加独特。

FedSym：释放熵的力量以便对联邦学习算法进行基准测试