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Multicenter Hierarchical Federated Learning With Fault-Tolerance Mechanisms for Resilient Edge Computing Networks.
IEEE Transactions on Neural Networks and Learning Systems
|March 28, 2024
Summary
The multicenter hierarchical federated learning (MCHFL) framework enhances robustness by distributing aggregation centers at the edge. This approach maintains high accuracy even with 50% device failures, outperforming traditional single-center federated learning models.
Area of Science:
- Artificial Intelligence
- Distributed Systems
- Machine Learning
Background:
- Traditional federated learning (FL) relies heavily on a central server, posing risks of communication bottlenecks and security vulnerabilities.
- Existing hierarchical FL (HFL) models offer some decentralization but lack complete edge-centric aggregation and full fault tolerance.
- Edge computing environments require robust FL solutions that can withstand device and server malfunctions.
Purpose of the Study:
- To introduce a novel multicenter hierarchical federated learning (MCHFL) framework designed for enhanced fault tolerance and decentralization.
- To address the limitations of single-center FL architectures in edge computing scenarios.
- To develop an edge-centric FL model aggregation strategy that improves robustness.
Main Methods:
- Proposed the multicenter HFL (MCHFL) framework, replacing a single central server with distributed edge-based global aggregation centers.
- Conducted experiments using MNIST, FashionMNIST, and CIFAR-10 datasets to evaluate MCHFL performance.
- Assessed MCHFL's accuracy and robustness under high paralysis ratios (up to 50%).
Main Results:
- MCHFL demonstrated superior performance compared to traditional single-center models, maintaining high accuracy under significant device failures.
- Maximum accuracy reductions were limited to 2.60% (MNIST), 5.12% (FashionMNIST), and 16.73% (CIFAR-10) at 50% paralysis.
- MCHFL exhibited faster convergence speeds and stronger robustness in extensive experimental validation.
Conclusions:
- The MCHFL framework represents a pioneering paradigm in edge multicenter FL, offering significant improvements in fault tolerance and performance.
- The study provides the first known edge multicenter FL framework with theoretical underpinnings.
- MCHFL is validated as a highly effective solution for robust and efficient federated learning in edge environments.
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