Search Results for author:
Found 6 papers, 0 papers with code
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity
First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity.
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data
Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks.
Towards Robust Federated Learning via Logits Calibration on Non-IID Data
Meanwhile, the non-independent and identically distributed (non-IID) challenge of data distribution between edge devices can further degrade the performance of models.
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
The existing single prototype-based strategy represents a class by using the mean of the feature space.
Impact Learning: A Learning Method from Features Impact and Competition
This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features.
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
