Federated learning for computationally constrained heterogeneous devices: A survey
With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …
field, offloading training of neural networks (NNs) to a central server becomes more and …
Federated learning for inference at anytime and anywhere
Federated learning has been predominantly concerned with collaborative training of deep
networks from scratch, and especially the many challenges that arise, such as …
networks from scratch, and especially the many challenges that arise, such as …
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Federated learning (FL) is usually performed on resource-constrained edge devices, eg,
with limited memory for the computation. If the required memory to train a model exceeds …
with limited memory for the computation. If the required memory to train a model exceeds …
Fedzero: Leveraging renewable excess energy in federated learning
Federated Learning (FL) is an emerging machine learning technique that enables
distributed model training across data silos or edge devices without data sharing. Yet, FL …
distributed model training across data silos or edge devices without data sharing. Yet, FL …
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
Federated Learning (FL) allows machine learning models to train locally on individual
mobile devices, synchronizing model updates via a shared server. This approach …
mobile devices, synchronizing model updates via a shared server. This approach …
CoCoFL: Communication-and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
Devices participating in federated learning (FL) typically have heterogeneous
communication, computation, and memory resources. However, in synchronous FL, all …
communication, computation, and memory resources. However, in synchronous FL, all …
Model-Heterogeneous Federated Learning for Internet of Things: Enabling Technologies and Future Directions
Internet of Things (IoT) interconnects a massive amount of devices, generating
heterogeneous data with diverse characteristics. IoT data emerges as a vital asset for data …
heterogeneous data with diverse characteristics. IoT data emerges as a vital asset for data …
EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
The rise of mobile devices with abundant sensory data and local computing capabilities has
driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) …
driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) …
FedHC: A Scalable Federated Learning Framework for Heterogeneous and Resource-Constrained Clients
Federated Learning (FL) is a distributed learning paradigm that empowers edge devices to
collaboratively learn a global model leveraging local data. Simulating FL on GPU is …
collaboratively learn a global model leveraging local data. Simulating FL on GPU is …
Towards AI-Native Vehicular Communications
The role of fast yet reliable wireless communications in various application domains is
getting ever more important. At the same time, as use cases are becoming more and more …
getting ever more important. At the same time, as use cases are becoming more and more …