A survey of recent advances in optimization methods for wireless communications
Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …
solution tool for the design of wireless communications systems. While optimization has …
Enhancing generalization in federated learning with heterogeneous data: A comparative literature review
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 …
Learning (ML) model is trained using typically private and distributed data sources without …
A survey of advances in optimization methods for wireless communication system design
Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …
solution tool for the design of wireless communications systems. While optimization has …
Communication-efficient federated learning with single-step synthetic features compressor for faster convergence
Reducing communication overhead in federated learning (FL) is challenging but crucial for
large-scale distributed privacy-preserving machine learning. While methods utilizing …
large-scale distributed privacy-preserving machine learning. While methods utilizing …
Making batch normalization great in federated deep learning
Batch Normalization (BN) is widely used in {centralized} deep learning to improve
convergence and generalization. However, in {federated} learning (FL) with decentralized …
convergence and generalization. However, in {federated} learning (FL) with decentralized …
Fedwon: Triumphing multi-domain federated learning without normalization
Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …
Is normalization indispensable for multi-domain federated learning?
Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …
A privacy preserving system for movie recommendations using federated learning
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 …
choice problem faced by many users, and are utilized by many online businesses to drive …
Normalization is all you need: Understanding layer-normalized federated learning under extreme label shift
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 …
of foundation models. Recently, LN has been shown to be surprisingly effective in federated …
Decentralized data-privacy preserving deep-learning approaches for enhancing inter-database generalization in automatic sleep staging
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 …
the area remains a focus of research interest. Indeed, while promising results have reported …