A survey on federated learning for resource-constrained IoT devices
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …
model by learning from multiple decentralized edge clients. FL enables on-device training …
Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
Data-free knowledge distillation for heterogeneous federated learning
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …
server iteratively averages the model parameters of local users without accessing their data …
Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …
solutions to replace the traditional model-driven approaches that proved to be not rich …
A survey on security and privacy of federated learning
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …
learning with non-IID data. To cope with this, most of the existing works involve enforcing …
Parameterized knowledge transfer for personalized federated learning
In recent years, personalized federated learning (pFL) has attracted increasing attention for
its potential in dealing with statistical heterogeneity among clients. However, the state-of-the …
its potential in dealing with statistical heterogeneity among clients. However, the state-of-the …
Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
A survey on federated learning systems: Vision, hype and reality for data privacy and protection
As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …
been a hot research topic in enabling the collaborative training of machine learning models …
Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data
This study develops a federated learning (FL) framework overcoming largely incremental
communication costs due to model sizes in typical frameworks without compromising model …
communication costs due to model sizes in typical frameworks without compromising model …