Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models,
and non-iid labels scenarios. Existing FL methods fail to handle the above three constraints …

Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed
way, allowing for privacy protection. However, the real-world non-IID data will lead to client …

Federated Learning (FL) enables model development by leveraging data distributed across
numerous edge devices without transferring local data to a central server. However, existing …

Federated Learning (FL) aims to generate a global shared model via collaborating
decentralized clients with privacy considerations. Unlike standard distributed optimization …

Federated Learning (FL) is emerging as a popular, promising decentralized learning
framework that enables collaborative training among clients, with no need to share private …

Recently, federated learning (FL) is popular for its privacy-preserving and collaborative
learning abilities. However, under statistically heterogeneous scenarios, we observe that …

Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm
in which clients enable collaborative training without compromising private data. However …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning (FL) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …

Federated Learning (FL) has emerged as a promising distributed learning paradigm that
enables multiple clients to learn a global model collaboratively without sharing their private …