Summary of Fedaq: Communication-efficient Federated Edge Learning Via Joint Uplink and Downlink Adaptive Quantization, by Linping Qu et al.
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
by Linping Qu, Shenghui Song, Chi-Ying Tsui
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
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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 This paper explores federated learning (FL) in resource-limited wireless networks, where communication overhead is a significant challenge. FL leverages client data while protecting privacy, but substantial model sizes and frequent aggregations lead to high communication demands. To mitigate this, the authors propose using quantization techniques, building upon previous work that focused on uplink communication. This paper introduces a holistic approach by jointly optimizing uplink and downlink adaptive quantization for optimal learning convergence under energy constraints. Theoretical analysis shows that optimal quantization levels depend on model gradient or weight ranges, leading to proposed decreasing-trend quantization for the uplink and increasing-trend quantization for the downlink. Experimental results demonstrate a significant energy savings of up to 66.7% compared to existing schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning more efficient in situations where devices have limited power or data plans. The current approach to machine learning, called federated learning, can use lots of energy and data because it requires sending large amounts of information between devices. To solve this problem, the authors suggest reducing the amount of data sent by using a technique called quantization. They propose a new way of doing this that combines two types of quantization: one for sending data from devices to the main server and another for receiving updates from the server back to the devices. The results show that this approach can save up to 66.7% energy compared to current methods. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Quantization
