Research Area:  Machine Learning
Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.
Author(s) Name:  Viraj Kulkarni; Milind Kulkarni; Aniruddha Pant
Conferrence name:  2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)
Publisher name:  IEEE
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9210355