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The deployment of deep learning models for real-time image classification on resource-constrained sensor devices presents significant challenges. These devices face strict limitations in computational power, energy capacity, and memory resources, making it difficult to achieve both high accuracy and low latency. Current approaches either compromise model performance through compression or incur substantial overhead by offloading computation to remote servers. We introduce a novel distributed progressive inference platform that addresses these limitations by dynamically balancing local and remote computation. Our system employs reinforcement learning to make intelligent decisions about when and where to perform inference. Experimental results across multiple standard datasets demonstrate that our approach achieves up to 3% higher accuracy while reducing network traffic and preserving battery life compared to existing methods.
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Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. However, existing pruning approaches heavily rely on training data for guiding the pruning strategies, making them ineffective for federated learning over distributed and confidential datasets. Additionally, the memory- and computation-intensive pruning process becomes infeasible for recourse-constrained devices in federated learning. To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory-and computing-constrained devices. We introduce two key modules in FedTiny to adaptively search coarse- and finer-pruned specialized models to fit deployment scenarios with sparse and cheap local computation. First, an adaptive batch normalization selection module is designed to mitigate biases in pruning caused by the heterogeneity of local data. Second, a lightweight progressive pruning module aims to finer prune the models under strict memory and computational budgets, allowing the pruning policy for each layer to be gradually determined rather than evaluating the overall model structure. The experimental results demonstrate the effectiveness of FedTiny, which outperforms state-of-the-art approaches, particularly when compressing deep models to extremely sparse tiny models. FedTiny achieves an accuracy improvement of 2.61% while significantly reducing the computational cost by 95.91% and the memory footprint by 94.01% compared to state-of-the-art methods.
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- Award ID(s):
- 2151238
- PAR ID:
- 10512065
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
- ISBN:
- 979-8-3503-3986-4
- Page Range / eLocation ID:
- 190 to 201
- Format(s):
- Medium: X
- Location:
- Hong Kong, Hong Kong
- Sponsoring Org:
- National Science Foundation
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