Leveraging scalable data parallelism and effective model parameter aggregation, Federated
Learning has been widely used to unite resource-constrained devices for neural network …

Federated learning (FL) trains machine learning (ML) models on devices using locally
generated data and exchanges models without transferring raw data to a distant server. This …

Federated learning (FL) enables edge devices to cooperatively train models without
exposing their raw data. However, implementing a practical FL system at the network edge …

In this paper, we propose and analyze HierAdMo, a three-tier adaptive momentum
accelerated client-edge-cloud Federated Learning (FL) algorithm. HierAdMo combines the …

Most Federated Learning (FL) algorithms proposed to date obtain the global model by
aggregating multiple local models that typically share the same architecture, thus …

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

Federated learning (FL) is a machine learning system that allows a network of devices to
train a model without centralized data. This characteristic makes FL an ideal choice for …

In practical federated learning scenarios, the participating devices may have different
bitwidths for computation and memory storage by design. However, despite the progress …