Search Results for author:
Found 4 papers, 0 papers with code
Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity
This paper presents a novel hierarchical federated learning algorithm within multiple sets that incorporates quantization for communication-efficiency and demonstrates resilience to statistical heterogeneity.
Federated Learning via Lattice Joint Source-Channel Coding
This paper introduces a universal federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme.
Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity
When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial.
Scalable Hierarchical Over-the-Air Federated Learning
When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
