Summary of Leveraging Federated Learning and Edge Computing For Recommendation Systems Within Cloud Computing Networks, by Yaqian Qi et al.
Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks
by Yaqian Qi, Yuan Feng, Xiangxiang Wang, Hanzhe Li, Jingxiao Tian
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A combination of artificial intelligence and edge computing enables Edge Intelligence, processing data closer to its generation. Federated Learning (FL) allows data owners to train models without sharing raw data, but FL networks with thousands of devices face communication bottlenecks. To address this, a Hierarchical Federated Learning (HFL) framework is proposed, aggregating models through intermediate leaders and optimizing edge server resource utilization. The authors also model user quality of experience (QoE) as a system cost and propose a decentralized caching algorithm using federated deep reinforcement learning (DRL) and FL. This paper aims to mitigate the impact of soft clicks on QoE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edge Intelligence combines AI and edge computing, allowing data processing closer to its generation. Federated Learning helps train models without sharing raw data, but large networks face communication issues. A new framework, Hierarchical Federated Learning, optimizes edge server resources and solves this problem. The authors also look at how users experience their online interactions and propose a way to make it better. |
Keywords
» Artificial intelligence » Federated learning » Reinforcement learning
