Federated Meta Learning

Federated meta-learning combines the benefits of federated learning (distributed model training preserving data privacy) and meta-learning (learning to learn, enabling rapid adaptation to new tasks or environments). Current research focuses on developing efficient algorithms, such as variations of Model-Agnostic Meta-Learning (MAML) and Reptile, to address challenges posed by data heterogeneity and limited communication bandwidth in federated settings, often incorporating techniques like partial model sharing or adaptive aggregation strategies. This approach holds significant promise for improving the performance and scalability of federated learning across diverse applications, particularly in resource-constrained environments like mobile and IoT devices, and for tasks with limited data availability, such as rare disease prediction or personalized medicine.