Federated learning (FL) enables collaborative model training among distributed devices
without data sharing, but existing FL suffers from poor scalability because of global model …

Federated learning (FL) enables collaborative model training among distributed devices
without data sharing, but existing FL suffers from poor scalability because of global model …

Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …

Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while ensuring data privacy. The selection of participating devices in each training round …

Federated Learning faces significant challenges in statistical and system heterogeneity,
along with high energy consumption, necessitating efficient client selection strategies …

This paper presents ProFL, a novel progressive FL framework to effectively break the
memory wall. Specifically, ProFL divides the model into different blocks based on its original …

In this paper, we propose SmartFreeze, a framework that effectively reduces the memory
footprint by conducting the training in a progressive manner. Instead of updating the full …

Federated Learning coordinates multiple clients to collaboratively train a shared model
while preserving data privacy. However, the training data with noisy labels located on the …

Real-world deployment of federated learning requires orchestrating clients with widely
varied compute resources, from strong enterprise-grade devices in data centers to weak …

Federated learning (FL), a decentralized machine learning approach, enables privacy-
preserving and collaborative model training without centralizing sensitive data. It has been …