Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

Personalized federated learning (PFL) has been widely investigated to address the
challenge of data heterogeneity, especially when a single generic model is inadequate in …

Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while kee** the training data locally. However, for …

Federated learning (FL) is an emerging distributed machine learning method that empowers
in-situ model training on decentralized edge devices. However, multiple simultaneous FL …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …

Cross-silo federated learning (FL) is a privacy-preserving distributed machine learning
where organizations acting as clients cooperatively train a global model without uploading …

Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …

The statistical heterogeneity of the non-independent and identically distributed (non-IID)
data in local clients significantly limits the performance of federated learning. Previous …

Federated learning is a distributed learning framework that operates effectively over wireless
networks. It enables devices to collaboratively train a model over wireless links by sharing …