Distributed machine learning for wireless communication networks: Techniques, architectures, and applications
Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …
learning, and distributed reinforcement learning, have been increasingly applied to wireless …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
Heterogeneous computation and resource allocation for wireless powered federated edge learning systems
Federated learning (FL) is a popular edge learning approach that utilizes local data and
computing resources of network edge devices to train machine learning (ML) models while …
computing resources of network edge devices to train machine learning (ML) models while …
Joint user association and resource allocation for wireless hierarchical federated learning with IID and non-IID data
In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is
proposed for large-scale model training while preserving data privacy. However, the …
proposed for large-scale model training while preserving data privacy. However, the …
Trustworthy federated learning via blockchain
The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving,
Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI …
Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI …
Energy-efficient resource management for federated edge learning with CPU-GPU heterogeneous computing
Q Zeng, Y Du, K Huang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Edge machine learning involves the deployment of learning algorithms at the network edge
to leverage massive distributed data and computation resources to train artificial intelligence …
to leverage massive distributed data and computation resources to train artificial intelligence …
Task-oriented sensing, computation, and communication integration for multi-device edge AI
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly
exploits the AI model split inference and integrated sensing and communication (ISAC) to …
exploits the AI model split inference and integrated sensing and communication (ISAC) to …
Federated dropout—a simple approach for enabling federated learning on resource constrained devices
Federated learning (FL) is a popular framework for training an AI model using distributed
mobile data in a wireless network. It features data parallelism by distributing the learning …
mobile data in a wireless network. It features data parallelism by distributing the learning …
Federated learning via over-the-air computation with statistical channel state information
Federated learning (FL) is a popular distributed learning paradigm, in which a global model
at a server learns private data of clients without data shared among clients or the server. In …
at a server learns private data of clients without data shared among clients or the server. In …
Beamforming vector design and device selection in over-the-air federated learning
In this paper, we consider a beamforming vector design and device selection problem in
over-the-air computation (AirComp) for federated learning. Since the learning performance …
over-the-air computation (AirComp) for federated learning. Since the learning performance …