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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 …
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 …
Fedbn: Federated learning on non-iid features via local batch normalization
The emerging paradigm of federated learning (FL) strives to enable collaborative training of
deep models on the network edge without centrally aggregating raw data and hence …
deep models on the network edge without centrally aggregating raw data and hence …
Federated learning for healthcare informatics
With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …
more healthcare data are becoming readily available from clinical institutions, patients …
Broadband analog aggregation for low-latency federated edge learning
To leverage rich data distributed at the network edge, a new machine-learning paradigm,
called edge learning, has emerged where learning algorithms are deployed at the edge for …
called edge learning, has emerged where learning algorithms are deployed at the edge for …
Towards federated learning at scale: System design
Federated Learning is a distributed machine learning approach which enables model
training on a large corpus of decentralized data. We have built a scalable production system …
training on a large corpus of decentralized data. We have built a scalable production system …
Leaf: A benchmark for federated settings
Modern federated networks, such as those comprised of wearable devices, mobile phones,
or autonomous vehicles, generate massive amounts of data each day. This wealth of data …
or autonomous vehicles, generate massive amounts of data each day. This wealth of data …
Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning
In distributed training of deep neural networks, parallel minibatch SGD is widely used to
speed up the training process by using multiple workers. It uses multiple workers to sample …
speed up the training process by using multiple workers. It uses multiple workers to sample …
One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis
Federated edge learning (FEEL) is a popular framework for model training at an edge server
using data distributed at edge devices (eg, smart-phones and sensors) without …
using data distributed at edge devices (eg, smart-phones and sensors) without …
Multi-armed bandit-based client scheduling for federated learning
By exploiting the computing power and local data of distributed clients, federated learning
(FL) features ubiquitous properties such as reduction of communication overhead and …
(FL) features ubiquitous properties such as reduction of communication overhead and …