Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
This addresses the challenge of statistical heterogeneity in federated learning for clients with diverse data distributions, representing an incremental improvement over prior methods.
The paper tackles the problem of data heterogeneity in federated learning by proposing a personalized model approach using hybrid pruning to find client-specific subnetworks, resulting in improved performance for clients with non-IID data.
The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server. However, learning a single global model might not work well for all clients participating in the FL under data heterogeneity. Therefore, the personalization of the global model becomes crucial in handling the challenges that arise with statistical heterogeneity and the non-IID distribution of data. Unlike prior works, in this work we propose a new approach for obtaining a personalized model from a client-level objective. This further motivates all clients to participate in federation even under statistical heterogeneity in order to improve their performance, instead of merely being a source of data and model training for the central server. To realize this personalization, we leverage finding a small subnetwork for each client by applying hybrid pruning (combination of structured and unstructured pruning), and unstructured pruning. Through a range of experiments on different benchmarks, we observed that the clients with similar data (labels) share similar personal parameters. By finding a subnetwork for each client ...