Fedcir: Client-invariant representation learning for federated non-iid features
Z Li, Z Lin, J Shao, Y Mao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated learning (FL) is a distributed learning paradigm that maximizes the potential of
data-driven models for edge devices without sharing their raw data. However, devices often …
data-driven models for edge devices without sharing their raw data. However, devices often …
Wireless Federated Learning over Resource-Constrained Networks: Digital versus Analog Transmissions
To enable wireless federated learning (FL) in communication resource-constrained
networks, two communication schemes, ie, digital and analog ones, are effective solutions …
networks, two communication schemes, ie, digital and analog ones, are effective solutions …
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed
learning framework. In this work, we focus on cross-silo FL, where clients become the model …
learning framework. In this work, we focus on cross-silo FL, where clients become the model …
Over-the-Air Fusion of Sparse Spatial Features for Integrated Sensing and Edge AI over Broadband Channels
The 6G mobile networks are differentiated from 5G by two new usage scenarios-distributed
sensing and edge AI. Their natural integration, termed integrated sensing and edge AI …
sensing and edge AI. Their natural integration, termed integrated sensing and edge AI …