Learning from distributed data without accessing them is undoubtedly a challenging and non-trivial task. Nevertheless, the necessity for distributed training of a statistical model has been increasing, due to the privacy concerns of local data owners and the cost in centralizing the massively distributed data. Federated learning (FL) is currently the de facto standard of training a machine learning model across heterogeneous data owners, without leaving the raw data out of local silos. Nevertheless, several challenges must be addressed in order for FL to be more practical in reality. Among these challenges, the statistical heterogeneity problem is the most significant and requires immediate attention. From the main objective of FL, three major factors can be considered as starting points -- \textit{parameter}, textit{mixing coefficient}, and \textit{local data distributions}. In alignment with the components, this dissertation is organized into three parts. In Chapter II, a novel personalization method, \texttt{SuPerFed}, inspired by the mode-connectivity is introduced. In Chapter III, an adaptive decision-making algorithm, \texttt{AAggFF}, is introduced for inducing uniform performance distributions in participating clients, which is realized by online convex optimization framework. Finally, in Chapter IV, a collaborative synthetic data generation method, \texttt{FedEvg}, is introduced, leveraging the flexibility and compositionality of an energy-based modeling approach. Taken together, all of these approaches provide practical solutions to mitigate the statistical heterogeneity problem in data-decentralized settings, paving the way for distributed systems and applications using collaborative machine learning methods.

本研究针对在统计异质性条件下的协作机器学习中的数据分布问题，提出了三种创新算法以应对这一挑战。研究表明，所提出的方法可以有效缓解数据去中心化环境中的统计异质性问题，增强了协作学习系统的实用性和效率。

统计异质性下的协作机器学习算法