Devices participating in federated learning (FL) typically have heterogeneous
communication, computation, and memory resources. However, in synchronous FL, all …

Federated learning (FL) is able to manage edge devices to cooperatively train a model while
maintaining the training data local and private. One common assumption in FL is that all …

Federated learning (FL) is a promising distributed paradigm, eliminating the need for data
sharing but facing challenges from data heterogeneity. Personalized parameter generation …

When the available hardware cannot meet the memory and compute requirements to
efficiently train high performing machine learning models, a compromise in either the …

Driven by emerging technologies such as edge computing and Internet of Things (IoT),
recent years have witnessed the increasing growth of data processing in a distributed way …

Federated Learning (FL) offers a collaborative training framework, allowing multiple clients
to contribute to a shared model without compromising data privacy. Due to the …

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …

Federated Learning (FL) allows several clients to cooperatively train machine learning
models without disclosing the raw data. In practice, due to the system and statistical …

Federated learning (FL) supports training models on geographically distributed devices.
However, traditional FL systems adopt a centralized synchronous strategy, putting high …

Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical
challenges, ie, limited computational resources and low training efficiency. In this paper, we …