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Found 9 papers, 0 papers with code
Accelerating DNN Training in Wireless Federated Edge Learning Systems
The optimal solution in this scenario is manifested to have the similar structure as that of the CPU scenario, recommending that our proposed algorithm is applicable in more general systems.
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning
Specifically, the metrics that measure data importance in active learning (e. g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates.
A New Distributed Method for Training Generative Adversarial Networks
This paper proposes a new framework for training GANs in a distributed fashion: Each device computes a local discriminator using local data; a single server aggregates their results and computes a global GAN.
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network
Out-of-Distribution Detection
+1
Knowledge Base Enabled Semantic Communication: A Generative Perspective
Semantic communication is widely touted as a key technology for propelling the sixth-generation (6G) wireless networks.
Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding
Building on this, we design a visual program that consists of three types of modules, i. e., view-independent, view-dependent, and functional modules.
Scalable Federated Unlearning via Isolated and Coded Sharding
Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models.
Blockchain-enabled Trustworthy Federated Unlearning
However, existing works require central servers to retain the historical model parameters from distributed clients, such that allows the central server to utilize these parameters for further training even, after the clients exit the training process.
