Federated learning (FL) has become de facto framework for collaborative learning among
edge devices with privacy concern. The core of the FL strategy is the use of stochastic …

Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed
learning with a network of clients (ie, edge devices). However, the data distribution among …

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for
distributed learning. In this work, we mainly focus on the optimization of computation and …

Federated learning (FL) coordinates with numerous heterogeneous devices to
collaboratively train a shared model while preserving user privacy. Despite its multiple …

Federated learning is an emerging distributed machine learning framework which jointly
trains a global model via a large number of local devices with data privacy protections. Its …

Federated Learning (FL) has recently received a lot of attention for large-scale privacy-
preserving machine learning. However, high communication overheads due to frequent …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

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 become one of the mainstream paradigms for multi-party
collaborative learning with privacy protection. As it is difficult to guarantee all FL devices to …

Federated learning (FL) is a promising framework for privacy-preserving collaborative
learning, where model training tasks are distributed to clients and only the model updates …