Personalized Federated Learning for Statistical Heterogeneity

arXiv:2402.10254v1 Announce Type: new
Abstract: The popularity of federated learning (FL) is on the rise, along with growing concerns about data privacy in artificial intelligence applications. FL facilitates collaborative multi-party model learning while simultaneously ensuring the preservation of data confidentiality. Nevertheless, the problem of statistical heterogeneity caused by the presence of diverse client data distributions gives rise to certain challenges, such as inadequate personalization and slow convergence. In order to address the above issues, this paper offers a brief summary …

abstract applications artificial artificial intelligence arxiv client collaborative concerns cs.lg cs.ni data data privacy diverse federated learning intelligence personalized preservation privacy statistical type