Federated learning in edge computing: a systematic survey
Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …
From distributed machine learning to federated learning: A survey
In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …
users, various regions or organizations. Because of laws or regulations, the distributed data …
Federated learning for healthcare domain-pipeline, applications and challenges
M Joshi, A Pal, M Sankarasubbu - ACM Transactions on Computing for …, 2022 - dl.acm.org
Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …
distributed across data centers such as hospitals, clinical research labs, and mobile devices …
Responsible AI pattern catalogue: A collection of best practices for AI governance and engineering
Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific
challenges of our time and is key to increase the adoption of Artificial Intelligence (AI) …
challenges of our time and is key to increase the adoption of Artificial Intelligence (AI) …
Toward trustworthy ai: Blockchain-based architecture design for accountability and fairness of federated learning systems
Federated learning is an emerging privacy-preserving AI technique where clients (ie,
organizations or devices) train models locally and formulate a global model based on the …
organizations or devices) train models locally and formulate a global model based on the …
Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
Lightweight federated learning for rice leaf disease classification using non independent and identically distributed images
Rice (Oryza sativa L.) is a vital food source all over the world, contributing 15% of the protein
and 21% of the energy intake per person in Asia, where most rice is produced and …
and 21% of the energy intake per person in Asia, where most rice is produced and …
Comparative review of the intrusion detection systems based on federated learning: Advantages and open challenges
E Fedorchenko, E Novikova, A Shulepov - Algorithms, 2022 - mdpi.com
In order to provide an accurate and timely response to different types of the attacks, intrusion
and anomaly detection systems collect and analyze a lot of data that may include personal …
and anomaly detection systems collect and analyze a lot of data that may include personal …
Enabling federated learning across the computing continuum: Systems, challenges and future directions
In recent years, as the boundaries of computing have expanded with the emergence of the
Internet of Things (IoT) and its increasing number of devices continuously producing flows of …
Internet of Things (IoT) and its increasing number of devices continuously producing flows of …
[PDF][PDF] Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Responsible AI has been widely considered as one of the greatest scientific challenges of
our time and the key to increase the adoption of AI. A number of AI ethics principles …
our time and the key to increase the adoption of AI. A number of AI ethics principles …