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The recent developments in Federated Learning (FL) focus on optimizing the learning process for data, hardware, and model heterogeneity. However, most approaches assume all devices are stationary, charging, and always connected to the Wi-Fi when training on local data. We argue that when real devices move around, the FL process is negatively impacted and the device energy spent for communication is increased. To mitigate such effects, we propose a dynamic community selection algorithm which improves the communication energy efficiency and two new aggregation strategies that boost the learning performance in Hierarchical FL (HFL). For real mobility traces, we show that compared to state-of-the-art HFL solutions, our approach is scalable, achieves better accuracy on multiple datasets, converges up to 3.88× faster, and is significantly more energy efficient for both IID and non-IID scenarios.
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This content will become publicly available on March 26, 2026
Enhancing Data Security in Federated Learning with Dilithium
Federated learning (FL) enables multiple parties to collaboratively train machine learning models while preserving data privacy. However, securing communication within FL frameworks remains a significant challenge due to potential vulnerabilities to data breaches and integrity attacks. This paper proposes a novel approach using Dilithium, a robust digital signature framework, to enhance data security in FL. By integrating Dilithium into FL protocols, this study demonstrates robust communication security, preventing data tampering and unauthorized access, thereby promoting safer and more efficient collaborative model training across distributed networks. Furthermore, our approach incorporates an optimized client selection algorithm and a parallelized GPU-based training process that reduces latency and ensures seamless synchronization among participants. Experimental results demonstrate that our system achieves a total processing time of 6.891 seconds, significantly outperforming the 10.24 seconds of normal FL and 12.32 seconds of FL-Dilithium systems on the same computing platforms. Additionally, the proposed model achieves an accuracy of 94%, surpassing the 93% of the normal FL.
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- Award ID(s):
- 2348464
- PAR ID:
- 10579464
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- Proceedings of IEEE International Symposium on Consumer Electronics
- ISSN:
- 2158-4001
- ISBN:
- 979-8-3315-2116-5
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Las Vegas, NV, USA
- Sponsoring Org:
- National Science Foundation
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