Joint device scheduling and resource allocation for latency constrained wireless federated learning
In federated learning (FL), devices contribute to the global training by uploading their local
model updates via wireless channels. Due to limited computation and communication …
model updates via wireless channels. Due to limited computation and communication …
Efficiency-Boosting Federated Learning in Wireless Networks: A Long-Term Perspective
Federated learning (FL) can train a global model from clients' local dataset, which can make
full use of the computing resources of clients and performs more extensive and efficient …
full use of the computing resources of clients and performs more extensive and efficient …
Client selection and bandwidth allocation for federated learning: An online optimization perspective
Federated learning (FL) can train a global model from clients' local data set, which can make
full use of the computing resources of clients and performs more extensive and efficient …
full use of the computing resources of clients and performs more extensive and efficient …
Efficient wireless federated learning with partial model aggregation
The data heterogeneity across clients and the limited communication resources, eg,
bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL) …
bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL) …
Base station dataset-assisted broadband over-the-air aggregation for communication-efficient federated learning
This paper proposes an over-the-air aggregation framework for federated learning (FL) in
broadband wireless networks where not only edge devices but also a base station (BS) has …
broadband wireless networks where not only edge devices but also a base station (BS) has …
Joint user scheduling and resource allocation for federated learning over wireless networks
Federated learning (FL) is a decentralized algorithm that can train a globally shared model
without the requirement to send the raw data to a centralized server by user equipments …
without the requirement to send the raw data to a centralized server by user equipments …
Federated learning over wireless networks: Convergence analysis and resource allocation
There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …
Federated Learning (FL), in which the model training is distributed over mobile user …
Device scheduling and resource allocation for federated learning under delay and energy constraints
Federated Learning (FL) is an emerging technique to enhance edge intelligence, where
mobile devices train machine learning models collaboratively with their local data. Limited …
mobile devices train machine learning models collaboratively with their local data. Limited …
User assignment and resource allocation for hierarchical federated learning over wireless networks
The large population of wireless users is a key driver of data-crowdsourced Machine
Learning (ML). However, data privacy remains a significant concern. Federated Learning …
Learning (ML). However, data privacy remains a significant concern. Federated Learning …
Device scheduling for wireless federated learning with latency and representativity
Existing device scheduling methods in wireless fed-erated learning (FL) mainly focused on
selecting the devices with maximum gradient norm or loss function and requires all devices …
selecting the devices with maximum gradient norm or loss function and requires all devices …