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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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CHESSFL: Clustering Hierarchical Embeddings for Semi-Supervised Federated Learning
Hierarchical Federated Learning (HFL) has shown great promise over the past few years, with significant improvements in communication efficiency and overall performance. However, current research for HFL predominantly centers on supervised learning. This focus becomes problematic when dealing with semi-supervised learning, particularly under non-IID scenarios. In order to address this gap, our paper critically assesses the performance of straightforward adaptations of current state-of-the-art semi-supervised FL (SSFL) techniques within the HFL framework. We also introduce a novel clustering mechanism for hierarchical embeddings to alleviate the challenges introduced by semi-supervised paradigms in a hierarchical setting. Our approach not only provides superior accuracy, but also converges up to 5.11× faster, while being robust to non-IID data distributions for multiple datasets with negligible communication overhead
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- Award ID(s):
- 2107085
- PAR ID:
- 10547271
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7025-6
- Page Range / eLocation ID:
- 122 to 133
- Subject(s) / Keyword(s):
- Hierarchical Federated Learning, Semi-Supervised Learning, Edge Devices, Data Heterogeneity, Data Privacy, Communication Efficiency, Internet-of-Things
- Format(s):
- Medium: X
- Location:
- Hong Kong, Hong Kong
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IoTDI61053.2024.00015
