Owing to the nature of privacy protection, federated recommender systems (FedRecs) have
garnered increasing interest in the realm of on-device recommender systems. However …

Federated learning (FL) is a distributed machine learning approach in which many clients
contribute to learning a single global model in a privacy-preserving manner on the server …

Distributed machine learning methods like Federated Learning (FL) and Split Learning (SL)
meet the growing demands of processing large-scale datasets under privacy restrictions …

With the growing concerns regarding user data privacy, Federated Recommender System
(FedRec) has garnered significant attention recently due to its privacy-preserving …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

This paper presents a Federated Learning (FL) algorithm that allows the decentralization of
all FL solutions that employ a model-averaging procedure. The proposed algorithm proves …

In the ever-expanding Internet of Things (IoT) domain, the production of data has reached an
unparalleled scale. This massive data is processed to glean invaluable insights …

Federated learning (FL) is becoming a major driving force behind machine learning as a
service, where customers (clients) collaboratively benefit from shared local updates under …

The advent of federated learning (FL) has presented a viable solution for distributed training
in edge environment, while simultaneously ensuring the preservation of privacy. In real …

AIoT applications often encounter challenges such as terminal resource constraints, data
drift, and data heterogeneity in real world, leading to problems such as catastrophic …