Federated Learning (FL) is a collaborative training paradigm whereby a global Machine Learning (ML) model is trained using typically private and distributed data sources without …
Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has …
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing …
Batch Normalization (BN) is widely used in {centralized} deep learning to improve convergence and generalization. However, in {federated} learning (FL) with decentralized …
W Zhuang, L Lyu - The Twelfth International Conference on …, 2024 - openreview.net
Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …
W Zhuang, L Lyu - … Workshop on Federated Learning for Distributed …, 2023 - openreview.net
Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …
D Neumann, A Lutz, K Müller, W Samek - ACM Transactions on …, 2023 - dl.acm.org
Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive …
G Zhang, M Beitollahi, A Bie, X Chen - arXiv preprint arXiv:2308.09565, 2023 - arxiv.org
Layer normalization (LN) is a widely adopted deep learning technique especially in the era of foundation models. Recently, LN has been shown to be surprisingly effective in federated …
A Anido-Alonso… - IEEE Journal of Biomedical …, 2023 - ieeexplore.ieee.org
Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, while promising results have reported …