Authors: Sana Hafeez, Lina Mohjazi, Muhammad Ali Imran, Yao Sun

Published on: February 07, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.05973

Summary

• What is new: The introduction of a Blockchain-enabled Clustered and Scalable Federated Learning framework (BCS-FL) for UAV networks, which improves decentralization, coordination, scalability, and efficiency.
• Why this is important: Privacy, scalability, and reliability in UAV networks using ML technologies, complicated by issues like communication overhead, synchronization, scalability, and resource constraints.
• What the research proposes: A framework that partitions UAV networks into clusters with coordinated updates via cluster head UAVs, employing hybrid model aggregation for improved collaboration and efficiency.
• Results: Achieved convergence in model training with a discussion on the trade-offs between training effectiveness and communication efficiency.

Technical Details

Technological frameworks used: Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL)

Models used: Machine Learning models with hybrid inter-cluster and intra-cluster aggregation schemes

Data used: nan

Potential Impact

UAV manufacturing and service companies, telecommunication companies engaged in UAV networks, and industries relying on UAV-based data collection and analysis

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