Model optimization techniques in personalized federated learning: A survey.

Personalized federated learning (PFL) is an exciting approach that allows machine learning (ML) models to be trained on diverse and decentralized sources of data, while maintaining client privacy and autonomy. However, PFL faces several challenges that can deteriorate the performance and effectiveness of the learning process. These challenges include data heterogeneity, communication overhead, model privacy, model drift, client heterogeneity, label noise and imbalance, federated optimization challenges, and client participation and engagement. To address these challenges, researchers are exploring innovative techniques and algorithms that can enable efficient and effective PFL. These techniques include several optimization algorithms. This research survey provides an overview of the challenges and motivations related to the model optimization strategies for PFL, as well as the state-of-the-art (SOTA) methods and algorithms which seek to provide solutions of these challenges. Overall, this survey can be a valuable resource for researchers who are interested in the emerging field of PFL as well as its potential for personalized machine learning in a federated environment. • A Comprehensive Review of Contemporary Approaches in Personalized Federated Learning (PFL). • Categorization and classification of various methods and solutions to provide comprehensive taxonomies. • A diverse array of literature that addresses emerging challenges and cutting-edge solutions in the field. • This paper outlines and proposes new research directions that can be pursued by the interested community. [ABSTRACT FROM AUTHOR]