Exploiting unlabeled data in smart cities using federated edge learning
Privacy concerns are considered one of the main challenges in smart cities as sharing
sensitive data induces threatening problems in people's lives. Federated learning has …
sensitive data induces threatening problems in people's lives. Federated learning has …
Ssfl: Tackling label deficiency in federated learning via personalized self-supervision
Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-
the-cloud setting to distributed training over edge devices in order to strengthen data …
the-cloud setting to distributed training over edge devices in order to strengthen data …
Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
Federated learning (FL) plays an important role in the development of smart cities. With the
evolution of big data and artificial intelligence, issues related to data privacy and protection …
evolution of big data and artificial intelligence, issues related to data privacy and protection …
A survey on federated learning: The journey from centralized to distributed on-site learning and beyond
S AbdulRahman, H Tout… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …
Semi-supervised federated learning over heterogeneous wireless iot edge networks: Framework and algorithms
Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems
to underpin network edge intelligence for smart cities applications. However, most of the …
to underpin network edge intelligence for smart cities applications. However, most of the …
FedOVA: one-vs-all training method for federated learning with non-IID data
Federated Learning (FL) is a privacy-oriented framework that allows distributed edge
devices to jointly train a shared global model without transmitting their sensed data to …
devices to jointly train a shared global model without transmitting their sensed data to …
Federated learning in smart cities: Privacy and security survey
Over the last decade, smart cities (SC) have been developed worldwide. Implementing big
data and the internet of things improves the monitoring and integration of different …
data and the internet of things improves the monitoring and integration of different …
Evaluating the communication efficiency in federated learning algorithms
In the era of advanced technologies, mobile devices are equipped with computing and
sensing capabilities that gather excessive amounts of data. These amounts of data are …
sensing capabilities that gather excessive amounts of data. These amounts of data are …
Federated learning algorithm based on knowledge distillation
D Jiang, C Shan, Z Zhang - 2020 International conference on …, 2020 - ieeexplore.ieee.org
Federated learning is a new scheme of distributed machine learning, which enables a large
number of edge computing devices to jointly learn a shared model without private data …
number of edge computing devices to jointly learn a shared model without private data …
Towards federated learning against noisy labels via local self-regularization
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized
devices with labeled data in a privacy-preserving manner. However, data with noisy labels …
devices with labeled data in a privacy-preserving manner. However, data with noisy labels …