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Found 4 papers, 1 papers with code
FLex&Chill: Improving Local Federated Learning Training with Logit Chilling
Federated learning are inherently hampered by data heterogeneity: non-iid distributed training data over local clients.
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
However, most existing FL works are not conducted on datasets collected from authentic IoT devices that capture unique modalities and inherent challenges of IoT data.
On-device Training: A First Overview on Existing Systems
A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation.
Fast Monte-Carlo Approximation of the Attention Mechanism
We introduce Monte-Carlo Attention (MCA), a randomized approximation method for reducing the computational cost of self-attention mechanisms in Transformer architectures.
