The convergence and generalization abilities of federated learning (FL) models encounter significant obstacles when confronted with non-independent and identically distributed (non-IID) data. This situation leads to wandering behaviors among ...

Federated Learning (FL) is a machine learning setting where multiple worker devices collaboratively train a model under the orchestration of a central server, while keeping the training data local. However, owing to the lack of supervision on worker ...

Personalized federated learning aims to address two key challenges in federated systems: performance degradation of the global model, and the lack of specificity to individual clients. Both of them are caused by client heterogeneity. Previous solutions ...

With the advance of technology, there is an increasing variety of Internet of Things (IoT) devices, which could be vulnerable to hacker attacks. Therefore, intrusion detection for these IoT devices is crucial. Federated learning, a novel distributed ...

Hierarchical federated learning has been studied as a more practical approach to federated learning in terms of scalability, robustness, and privacy protection, particularly in edge computing. To achieve these advantages, operations are typically ...

As the scale of federated learning expands, solving the Non‐IID data problem of federated learning has become a key challenge of interest. Most existing solutions generally aim to solve the overall performance improvement of all clients; however,...

Federated Learning (FL), also known as collaborative learning, is a distributed machine learning approach that collaboratively learns a shared prediction model without explicitly sharing private data. When dealing with sensitive data, privacy measures ...

Federated learning (FL) is a privacy-preserving paradigm for multi-institutional collaborations, where the aggregation is an essential procedure after training on the local datasets. Conventional aggregation algorithms often apply a weighted averaging of ...

Federated learning is a distributed machine learning framework where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training ...

Federated Learning (FL) enables learning a shared model acrossmany clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and ...