Summary of Fedsr: a Semi-decentralized Federated Learning Algorithm For Non-iidness in Iot System, by Jianjun Huang et al.
FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System
by Jianjun Huang, Lixin Ye, Li Kang
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 In the context of Industrial Internet of Things (IoT), federated learning has emerged as a viable solution to address privacy and security concerns. However, practical applications of federated learning are often hindered by data heterogeneity across devices, which can negatively impact model performance. Additionally, large-scale IoT deployments can be constrained by limited communication resources on cloud servers. To tackle these challenges, this paper proposes a semi-decentralized cloud-edge-device hierarchical federated learning framework that combines centralized and decentralized approaches to mitigate the effects of data heterogeneity. The proposed framework leverages an incremental subgradient optimization algorithm in each ring cluster to improve model generalization capabilities. Experimental results demonstrate the effectiveness of this approach in reducing the impact of data heterogeneity and alleviating communication bottlenecks on cloud servers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where lots of devices, like smart home appliances or industrial equipment, can learn from each other without sharing their sensitive information. This is called federated learning, and it’s crucial for the Industrial Internet of Things (IoT). However, making this work is tricky because data from different devices often looks very different, which can ruin the model’s performance. Moreover, when many devices are involved in training, it becomes hard to share information efficiently. To solve these problems, researchers have designed a new approach that combines two existing methods. They call it semi-decentralized cloud-edge-device hierarchical federated learning. It uses an algorithm to make each device’s model better and more generalizable. The results show that this method can effectively handle the differences in data and improve communication efficiency. |
Keywords
* Artificial intelligence * Federated learning * Generalization * Optimization
