Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

With the continuous development of web technology, Web3. 0 has attracted a considerable
amount of attention due to its unique decentralized characteristics. The digital economy is an …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

Federated learning (FL) has emerged as a promising paradigm for collaborative model
training while preserving data privacy. Client selection plays a crucial role in determining the …

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine
Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments …

Many compression techniques have been proposed to reduce the communication overhead
of Federated Learning training procedures. However, these are typically designed for …

Multimodal federated learning (FL) aims to enrich model training in FL settings where
devices are collecting measurements across multiple modalities (eg, sensors measuring …

Federated Learning (FL) requires frequent exchange of model parameters, which leads to
long communication delay, especially when the network environments of clients vary greatly …

Communication overhead has become one of the major bottlenecks in the distributed
training of modern deep neural networks. With such consideration, various quantization …