Federated Distillation: A Survey

arXiv:2404.08564v1 Announce Type: new
Abstract: Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients. Despite its promise, FL encounters challenges such as high communication costs for large-scale models and the necessity for uniform model architectures across all clients and the server. These challenges severely restrict the practical applications of FL. To address these limitations, the integration of knowledge distillation (KD) into FL has been proposed, forming what is known as Federated Distillation …

abstract applications architectures arxiv challenges communication costs cs.lg data distillation federated learning large-scale models practical scale server survey train training training data type uniform