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Autonomous Model Aggregation for Decentralized Learning on Edge Devices
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AI
DOI：10.1109/TPDS.2025.3621058.png)
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Abstract
En 中文
Edge AI applications enable edge devices to collaboratively learn a model via repeated model aggregations, aiming to utilize the distributed data on the devices for achieving high model accuracy. Existing methods either leverage a centralized server to directly aggregate the model updates from edge devices or need a central coordinator to group the edge devices for localized model aggregations. The centralized server (or coordinator) has a performance bottleneck and a high cost of collecting the global state needed for making the grouping decision in large-scale networks. In this paper, we propose an Autonomous Model Aggregation (AMA) method for large-scale decentralized learning on edge devices. Instead of needing a central coordinator to group the edge devices, AMA allows the edge devices to autonomously form groups using a highly efficient protocol, according to model functional similarity and historical grouping information. Moreover, AMA adopts a reinforcement learning approach to optimize the size of each group. Evaluation results on our self-developed edge computing testbed demonstrate that AMA outperforms the benchmark approaches by up to 20.71% in accuracy and reduced the convergence time by 75.58%.
Keywords:
Edge intelligence
distributed edge learning
model aggregation
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