In this paper, we investigate Federated Learning (FL), a paradigm of machine
learning that allows for decentralized model training on devices without
sharing raw data, there by preserving data privacy. In particular, we compare
two strategies within this paradigm: Federated Averaging (FedAvg) and
Personalized Federated Averaging (Per-FedAvg), focusing on their performance
with Non-Identically and Independently Distributed (Non-IID) data. Our analysis
shows that the level of data heterogeneity, modeled using a Dirichlet
distribution, significantly affects the performance of both strategies, with
Per-FedAvg showing superior robustness in conditions of high heterogeneity. Our
results provide insights into the development of more effective and efficient
machine learning strategies in a decentralized setting.

该研究调查了联邦学习（FL）这一机器学习范式，允许在不共享原始数据的设备上进行分散模型训练，从而保护数据隐私。我们比较了该范式内的两种策略：联邦平均（FedAvg）和个性化联邦平均（Per-FedAvg），重点关注它们在非独立同分布数据（Non-IID）条件下的性能。我们的分析显示，使用狄利克雷分布建模的数据异质性水平显著影响两种策略的性能，在高异质性条件下，Per-FedAvg 表现出更强的健壮性。我们的结果为在分散环境下开发更有效和高效的机器学习策略提供了见解。