首页|A hierarchical blockchain architecture for federated learning in edge computing networks
A hierarchical blockchain architecture for federated learning in edge computing networks
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NETL
NSTL
Springer Nature
Blockchain-based federated learning (FL) has recently garnered signifcant atten- tion as a trusted decentralized learning paradigm. However, traditional FL faces critical challenges: synchronous FL sufers from stragglers that delay training, while asynchronous FL risks model instability due to inconsistent updates. Moreover, processing blockchain consensus protocols incurs substantial resource consump- tion and operational latency. To overcome these challenges, we propose a hierar- chical blockchain architecture for semi-asynchronous FL that balances efciency and security. Our approach features a two-layer design: (1) a training layer, where edge nodes asynchronously upload local models via a directed acyclic graph (DAG) to mitigate stragglers and ensure continuous progress, and (2) a blockchain layer, which periodically validates and synchronously aggregates models to maintain sta- bility and defend against malicious inputs. We further introduce novel DAG-based transaction tracking and uploading algorithms to enhance efciency, enabling rapid local updates while ensuring global model integrity through blockchain consensus. Experimental results demonstrate that our system reduces latency by 26% com- pared to typical blockchain-based FL approaches, while maintaining a stable con- vergence rate and high training accuracy. By harmonizing asynchronous fexibility with synchronous control, our work enhances the scalability and robustness of FL in resource-constrained edge environments.
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, No.337, Jinhai Third Road, Economic & Technological Development Zone, Wenzhou 325025, China
Department of Computer Science, Hanyang University, 222 Wangsimni-ro Seongdong-gu, Seoul 04763, South Korea
NETL
NSTL
Springer Nature
