2024 Proceeding
No abstract available.
Proceeding Downloads
NestFL: Enhancing federated learning through nested multi-capacity model pruning in heterogeneous edge computing
Federated learning (FL) has been explored as a promising solution for distributed machine learning at the edge. However, the limited capacity and heterogeneity of edge devices usually bring FL with various critical challenges, such as Non-IID data, ...
Video Content Adaptive Transmission Technology Based on Reinforcement Learning
With the development of smart devices and network technology, video traffic has surged. Http Adaptive Streaming (HAS) technology, as a mature technology in this field, optimizes playback fluency through bandwidth prediction. However, existing bitrate ...
Cost-Aware Federated Learning in Mobile Edge Networks
Federated Learning (FL) allows multiple heterogeneous clients to cooperatively train models without disclosing private data. However, selfish clients may be unwilling to participate in FL training without any compensation. In addition, the ...
Index Terms
- Proceedings of the 3rd Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network