… the time cost of federated learning, rather than to achieve the best accuracy on this task,
we set target accuracy rates of 85%, 88%, and 90%, respectively. Besides, learning rate is an …

… In summary, compared with centralized learning or distributed learning, federated learning …
usages of federated learning in terms of different optimization objectives of network functions, …

… Finally, we build a collaborative federated learning architecture for anomaly traffic detection
in SAGIN to improve communication efficiency. The main contributions are as follows, …

… We propose a federation learning (FL) based algorithm to solve the embedding problem of
SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load …

… To address these issues, we propose to leverage the popular federated learning to train a …
Accordingly, we can integrate the standard synchronous federated learning (eg, the canonical …

… scalability of modern IoT networks and growing data privacy concerns. Federated Learning
(FL) … of the emerging applications of FL in IoT networks, beginning from an introduction to the …

… , we propose a framework called air-ground integrated federated learning (AGIFL), which
organically integrates air-ground integrated networks and federated learning (FL). In AGIFL, …

… These issues can be addressed using Federated Learning (FL) and blockchain. FL can be
used to address the issues of privacy preservation and handling big data generated in STI …

… In this paper, we propose a ground-to-satellite cooperative federated learning (FL)
methodology to facilitate machine learning service management over remote regions. Our …

… As a key paradigm of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) …
learning (ML) model training. Utilizing the satellite as the central server, federated learning (FL…