WebWhile federated learning is promising for privacy-preserving collaborative learning without revealing local data, it remains vulnerable to white-box attacks and struggles to adapt to heterogeneous clients. Federated distillation (FD), built upon knowledge distillation--an effective technique for transferring knowledge from a teacher model to student models- … WebIn this paper, to address these challenges, we are motivated to propose an incentive and knowledge distillation based federated learning scheme for crosssilo applications. Specifically, we first develop a new federated learning framework, to support cooperative learning among diverse heterogeneous client models. Second, we devise an incentive ...

Robust Multi-model Personalized Federated Learning via Model …

WebSep 29, 2024 · Label driven Knowledge Distillation for Federated Learning with non-IID Data. In real-world applications, Federated Learning (FL) meets two challenges: (1) … WebDec 5, 2024 · The first step is a knowledge distillation learning process in which all clients first initialise the task model on the local datasets and share it with neighbouring clients. ... The Distribution Information Sharing- and Knowledge Distillation-Based Cyclic Federated Learning Method. The ultimate goal of federated learning is to jointly train ... emmanuel macron walking on water

FedED: Federated Learning via Ensemble Distillation for …

WebMay 2, 2024 · The FedDKD introduces a module of decentralized knowledge distillation (DKD) to distill the knowledge of the local models to train the global model by … WebNov 4, 2024 · In this regard, federated distillation (FD) is a compelling distributed learning solution that only exchanges the model outputs whose dimensions are commonly much … WebFeb 3, 2024 · In this paper, we propose a novel federated learning scheme (Fig. 3), FedDKD, which introduces a module of decentralized knowledge distillation (DKD) to average the local models in the function space … emmanuel macron victory speech