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Found 7 papers, 1 papers with code
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation
This diversity can critically affect the training pace and the performance of models.
Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data
This article targets shallow models due to their interpretable nature to assess the presence of LiDAR data for supervised segmentation.
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset.
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning
We define a simple abstracted concept of isolation levels to capture the variations in isolation requirements and then formulate isolation-aware VNE as an optimization problem with resource and isolation constraints.
Towards General Deep Leakage in Federated Learning
We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images.
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities
DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process.
