Federated Virtual Learning on Heterogeneous Data with Local-global Distillation (2303.02278v2)

Abstract: Despite Federated Learning (FL)'s trend for learning machine learning models in a distributed manner, it is susceptible to performance drops when training on heterogeneous data. In addition, FL inevitability faces the challenges of synchronization, efficiency, and privacy. Recently, dataset distillation has been explored in order to improve the efficiency and scalability of FL by creating a smaller, synthetic dataset that retains the performance of a model trained on the local private datasets. We discover that using distilled local datasets can amplify the heterogeneity issue in FL. To address this, we propose a new method, called Federated Virtual Learning on Heterogeneous Data with Local-Global Distillation (FedLGD), which trains FL using a smaller synthetic dataset (referred as virtual data) created through a combination of local and global dataset distillation. Specifically, to handle synchronization and class imbalance, we propose iterative distribution matching to allow clients to have the same amount of balanced local virtual data; to harmonize the domain shifts, we use federated gradient matching to distill global virtual data that are shared with clients without hindering data privacy to rectify heterogeneous local training via enforcing local-global feature similarity. We experiment on both benchmark and real-world datasets that contain heterogeneous data from different sources, and further scale up to an FL scenario that contains large number of clients with heterogeneous and class imbalance data. Our method outperforms state-of-the-art heterogeneous FL algorithms under various settings with a very limited amount of distilled virtual data.