EdgeHive: Federated Learning on IoT Clusters in Unreliable Networks
Abstract
Federated Learning (FL) offers a decentralized approach to training machine learning models across distributed edge devices while preserving data privacy. However, traditional FL frameworks often assume reliable network connectivity and synchronous client participation assumptions that rarely hold in real world Internet of Things (IoT) environments characterized by intermittent connectivity, device dropout, and heterogeneous resources. In this paper, we propose EdgeHive, a robust and communication efficient FL framework specifically designed for deployment in unreliable edge networks. EdgeHive introduces a three tier architecture that organizes devices into dynamic micro clusters, enabling localized model aggregation and decentralized leader election to mitigate the effects of network instability. The framework incorporates adaptive scheduling, gossip based synchronization, and failure aware aggregation to ensure consistent learning despite high packet loss and client churn. Extensive experiments on real world IoT datasets demonstrate that EdgeHive achieves superior model accuracy, faster convergence, and lower communication overhead compared to state of the art FL methods such as FedAvg, FedProx, and ClusterFL. EdgeHive establishes a practical pathway for scalable, privacy preserving intelligence in next generation IoT deployments.
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- Authors
- Gupta, R.
- Corresponding author
- Gupta, Rashmi
- ORCID
- Gupta, Rashmi
- Venue
- 2025 13th International Conference on Intelligent Systems and Embedded Design (ISED)
- Publisher
- IEEE
- Published
- 2025-12-17
- Pages
- 758-763
- OpenAlex
- W7131821999
- Semantic Scholar
- 97cc6de46cf5431651bf6b6e35dbe87fe3f0eb3d
- Cited by
- 0
- References
- 15
- FWCI
- 0.00
- Type
- conference paper
- Open access
- closed
- Locations
- International Conference on Intelligent Systems and Embedded Design (ISED) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- federated learning, Internet of Things, unreliable networks, intermittent connectivity, microclusters, adaptive scheduling, communication efficiency, privacy-preserving machine learning
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