Survey of Personalization Techniques for Federated Learning
| Autor: | Aniruddha Pant, Milind Kulkarni, Viraj Kulkarni |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Information privacy Process (engineering) Computer science Machine Learning (stat.ML) Data science Machine Learning (cs.LG) Data modeling Personalization Study heterogeneity Incentive Work (electrical) Statistics - Machine Learning Task analysis |
| Zdroj: | 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). |
| DOI: | 10.1109/worlds450073.2020.9210355 |
| Popis: | 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. 4 pages |
| Databáze: | OpenAIRE |
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