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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 …
[HTML][HTML] Multimodal federated learning: A survey
Federated learning (FL), which provides a collaborative training scheme for distributed data
sources with privacy concerns, has become a burgeoning and attractive research area. Most …
sources with privacy concerns, has become a burgeoning and attractive research area. Most …
Federated learning in smart city sensing: Challenges and opportunities
Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …
[HTML][HTML] Federated learning for edge computing: A survey
New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …
network, allowing edge devices to train simple models that can then be deployed in practice …
[HTML][HTML] Applications of federated learning; taxonomy, challenges, and research trends
The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …
learning and deep learning models for edge network optimization. Although a complex edge …
A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications
Recent advances in deep learning have shown many successful stories in smart healthcare
applications with data-driven insight into improving clinical institutions' quality of care …
applications with data-driven insight into improving clinical institutions' quality of care …
Reviewing federated machine learning and its use in diseases prediction
Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …
automation and improved decision making in a variety of industries such as healthcare …
Federated learning for the internet-of-medical-things: A survey
Recently, in healthcare organizations, real-time data have been collected from connected or
implantable sensors, layered protocol stacks, lightweight communication frameworks, and …
implantable sensors, layered protocol stacks, lightweight communication frameworks, and …
FedACK: Federated adversarial contrastive knowledge distillation for cross-lingual and cross-model social bot detection
Social bot detection is of paramount importance to the resilience and security of online
social platforms. The state-of-the-art detection models are siloed and have largely …
social platforms. The state-of-the-art detection models are siloed and have largely …
[HTML][HTML] Blockchain-enabled asynchronous federated learning in edge computing
The fast proliferation of edge computing devices brings an increasing growth of data, which
directly promotes machine learning (ML) technology development. However, privacy issues …
directly promotes machine learning (ML) technology development. However, privacy issues …