Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Drew, Steve"'
Autor:
Dehghani, Farzaneh, Dibaji, Mahsa, Anzum, Fahim, Dey, Lily, Basdemir, Alican, Bayat, Sayeh, Boucher, Jean-Christophe, Drew, Steve, Eaton, Sarah Elaine, Frayne, Richard, Ginde, Gouri, Harris, Ashley, Ioannou, Yani, Lebel, Catherine, Lysack, John, Arzuaga, Leslie Salgado, Stanley, Emma, Souza, Roberto, Santos, Ronnie de Souza, Wells, Lana, Williamson, Tyler, Wilms, Matthias, Wahid, Zaman, Ungrin, Mark, Gavrilova, Marina, Bento, Mariana
Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents sign
Externí odkaz:
http://arxiv.org/abs/2408.15550
Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models c
Externí odkaz:
http://arxiv.org/abs/2408.12029
The top priority of a Housing and Homelessness System of Care (HHSC) is to connect people experiencing homelessness to supportive housing. An HHSC typically consists of many agencies serving the same population. Information technology platforms diffe
Externí odkaz:
http://arxiv.org/abs/2408.07845
Federated learning offers a compelling solution to the challenges of networking and data privacy within aerial and space networks by utilizing vast private edge data and computing capabilities accessible through drones, balloons, and satellites. Whil
Externí odkaz:
http://arxiv.org/abs/2406.17951
Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process. Cloud and edge servers hosting FL clients may exhibit diverse carbon foo
Externí odkaz:
http://arxiv.org/abs/2404.15503
Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private edge data from drones, balloons, and satellites. Existing research has
Externí odkaz:
http://arxiv.org/abs/2305.16351
Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is t
Externí odkaz:
http://arxiv.org/abs/2302.07305
The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending
Externí odkaz:
http://arxiv.org/abs/2302.03654
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massi
Externí odkaz:
http://arxiv.org/abs/2302.02573
Recently, a new distributed learning scheme called Federated Learning (FL) has been introduced. FL is designed so that server never collects user-owned data meaning it is great at preserving privacy. FL's process starts with the server sending a mode
Externí odkaz:
http://arxiv.org/abs/2211.14391