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) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Federated learning (FL) is an important paradigm for training global models from
decentralized data in a privacy-preserving way. Existing FL methods usually assume the …

Federated learning is a widely used distributed deep learning framework that protects the
privacy of each client by exchanging model parameters rather than raw data. However …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Federated Learning (FL) is a privacy-oriented framework that allows distributed edge
devices to jointly train a shared global model without transmitting their sensed data to …

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine
learning models with distributed data stored in different silos without exposing sensitive …

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 a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

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