FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
This addresses personalized federated learning for clients with local data, though it appears incremental as it builds on the lottery ticket hypothesis.
The paper tackles the problem of suboptimal personalization in federated learning by proposing FedSelect, a framework that customizes which parameters to fine-tune per client while preserving global knowledge, achieving promising results on CIFAR-10.
Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task. Existing methods for such personalization either prune a global model or fine-tune a global model on a local client distribution. However, these existing methods either personalize at the expense of retaining important global knowledge, or predetermine network layers for fine-tuning, resulting in suboptimal storage of global knowledge within client models. Enlightened by the lottery ticket hypothesis, we first introduce a hypothesis for finding optimal client subnetworks to locally fine-tune while leaving the rest of the parameters frozen. We then propose a novel FL framework, FedSelect, using this procedure that directly personalizes both client subnetwork structure and parameters, via the simultaneous discovery of optimal parameters for personalization and the rest of parameters for global aggregation during training. We show that this method achieves promising results on CIFAR-10.