Federated learning (FL) has emerged as a privacy-aware collaborative learning paradigm
where participants jointly train a powerful model without sharing their private data. One …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

With the development of trustworthy Federated Learning (FL), the requirement of
implementing right to be forgotten gives rise to the area of Federated Unlearning (FU) …

Federated Learning (FL) is designed to protect the data privacy of each client during the
training process by transmitting only models instead of the original data. However, the …

Federated unlearning has emerged very recently as an attempt to realize “the right to be
forgotten” in the context of federated learning. While the current literature is making efforts on …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

In federated learning (FL), a set of participants share updates computed on their local data
with an aggregator server that combines updates into a global model. However, reconciling …

Federated learning (FL) is a promising new technology in the field of IoT intelligence.
However, exchanging model-related data in FL may leak the sensitive information of …

Federated learning, as a typical distributed learning paradigm, shows great potential in
Industrial Internet of Things, Smart Home, Smart City, etc. It enables collaborative learning …