Summary of Shfl: Secure Hierarchical Federated Learning Framework For Edge Networks, by Omid Tavallaie et al.
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
by Omid Tavallaie, Kanchana Thilakarathna, Suranga Seneviratne, Aruna Seneviratne, Albert Y. Zomaya
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 The proposed Secure Hierarchical Federated Learning (SHFL) framework addresses model/data poisoning attacks in hierarchical edge networks. Building upon traditional FL frameworks, SHFL employs a two-level aggregation process at edge and cloud servers to improve resilience against malicious client devices. The framework consists of two novel methods: a client selection algorithm for choosing IoT devices to participate in training, and a model aggregation method based on convex optimization theory to reduce the impact of edge models from networks with adversaries. Evaluation results show that SHFL significantly increases the maximum accuracy achieved by the global model compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers propose a new way to make machine learning more secure for devices like smart home appliances or wearables. They want to protect these devices from being hacked and used to spread false information. The proposed system uses two levels of processing: one at the edge (where devices are connected) and another in the cloud. This helps reduce the impact of malicious devices trying to disrupt the learning process. The new approach is tested and shows significant improvements in accuracy compared to existing methods. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Optimization
