Efficient parallel split learning over resource-constrained wireless edge networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
Split learning in 6g edge networks
With the proliferation of distributed edge computing resources, the 6G mobile network will
evolve into a network for connected intelligence. Along this line, the proposal to incorporate …
evolve into a network for connected intelligence. Along this line, the proposal to incorporate …
HFEL: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning
Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …
privacy issue of mobile devices compared to conventional machine learning at the remote …
Autofl: Enabling heterogeneity-aware energy efficient federated learning
YG Kim, CJ Wu - MICRO-54: 54th Annual IEEE/ACM International …, 2021 - dl.acm.org
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …
collaboratively train a shared machine learning model, while keeping all the raw training …
Client selection for federated learning with heterogeneous resources in mobile edge
T Nishio, R Yonetani - ICC 2019-2019 IEEE international …, 2019 - ieeexplore.ieee.org
We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources for training …
technologies, which leverages distributed client data and computation resources for training …
Data-quality based scheduling for federated edge learning
FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-
preserving distributed training in wireless edge networks, where edge devices …
preserving distributed training in wireless edge networks, where edge devices …
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 …
Federated learning over wireless networks: Challenges and solutions
M Beitollahi, N Lu - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …
Semi-decentralized federated edge learning for fast convergence on non-IID data
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …
communication latency in Cloud-based machine learning solutions, while preserving data …
Communication-efficient federated learning for resource-constrained edge devices
Federated learning (FL) is an emerging paradigm to train a global deep neural network
(DNN) model by collaborative clients that store their private data locally through the …
(DNN) model by collaborative clients that store their private data locally through the …