Federated learning involves training statistical models over edge devices such as mobile
phones such that the training data is kept local. Federated Learning (FL) can serve as an …

Federated Learning (FL) allows parties to learn a shared prediction model by delegating the
training computation to clients and aggregating all the separately trained models on the …

In a decade, AI frontier research transitioned from the researcher's workstation to thousands
of high-end hardware-accelerated compute nodes. This rapid evolution shows no signs of …

We propose a novel protocol, Falkor, for secure aggregation for Federated Learning in the
multi-server scenario based on masking of local models via a stream cipher based on AES …

There is an urgent need for unifying data analytics as more and more application tasks
become more complex: Nowadays, it is normal to see tasks performing data preparation …

Federated Learning (FL) has emerged as a prospective solution that facilitates the training of
a high-performing centralised model without compromising the privacy of users. While …

Federated Learning (FL) is an emerging paradigm that enables intelligent agents to
collaboratively train Machine Learning (ML) models in a distributed manner, eliminating the …

Federated Learning is a machine learning technique where independent devices (clients)
cooperatively train a machine learning model by working on decentralized training data. A …

The increasing reliance on drone swarms for various applications necessitates robust real
time anomaly detection mechanisms to ensure operational security and efficiency …

Federated Learning (FL) is a strategy for training distributed learning models. This approach
gives rise to significant challenges including the non-independent and identically distributed …