Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

While centralized servers pose a risk of being a single point of failure, decentralized
approaches like blockchain offer a compelling solution by implementing a consensus …

Prevalent federated learning commonly develops under the assumption that the ideal global
class distributions are balanced. In contrast, real-world data typically follows the long-tailed …