A comprehensive survey on training acceleration for large machine learning models in IoT
The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …
Device sampling for heterogeneous federated learning: Theory, algorithms, and implementation
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …
across worker devices by having them train local models that are periodically aggregated by …
Multiple access techniques for intelligent and multi-functional 6G: Tutorial, survey, and outlook
Multiple access (MA) is a crucial part of any wireless system and refers to techniques that
make use of the resource dimensions to serve multiple users/devices/machines/services …
make use of the resource dimensions to serve multiple users/devices/machines/services …
Optimal rate adaption in federated learning with compressed communications
Federated Learning (FL) incurs high communication overhead, which can be greatly
alleviated by compression for model updates. Yet the tradeoff between compression and …
alleviated by compression for model updates. Yet the tradeoff between compression and …
On-device learning systems for edge intelligence: A software and hardware synergy perspective
Modern machine learning (ML) applications are often deployed in the cloud environment to
exploit the computational power of clusters. However, this in-cloud computing scheme …
exploit the computational power of clusters. However, this in-cloud computing scheme …
Random orthogonalization for federated learning in massive MIMO systems
We propose a novel communication design, termed random orthogonalization, for federated
learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The …
learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The …
Error-compensated sparsification for communication-efficient decentralized training in edge environment
Communication has been considered as a major bottleneck in large-scale decentralized
training systems since participating nodes iteratively exchange large amounts of …
training systems since participating nodes iteratively exchange large amounts of …
Device sampling and resource optimization for federated learning in cooperative edge networks
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …
across worker devices by having them train local models that are periodically aggregated by …
Security of federated learning in 6G era: A review on conceptual techniques and software platforms used for research and analysis
Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm enabling
multiple parties to train a model collaboratively without sharing their data. With the upcoming …
multiple parties to train a model collaboratively without sharing their data. With the upcoming …
Dual-objective personalized federated service system with partially-labeled data over wireless networks
Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-
based services and applications, and personalized federated learning (PFL) evolves to …
based services and applications, and personalized federated learning (PFL) evolves to …