通过智能缓存减少物联网联邦学习通信开销，提升效率与实用性。
Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments
- 采用FIFO、LRU和优先级缓存策略，筛选重要模型更新
- 在CIFAR-10和医疗数据集上降低通信量，准确率损失极小
- 适合智能城市、医疗等低延迟敏感场景部署
联邦学习（FL）使分布式设备可在不集中数据的情况下协同训练共享模型，但在资源受限环境中，通信成本仍是主要瓶颈。本文提出基于先进先出（FIFO）、最近最少使用（LRU）和优先级的缓存策略，通过有选择地转发关键模型更新，减少不必要的传输。在CIFAR-10和医学数据集上的实验表明，该方法显著降低通信开销，同时保持模型准确率。结果验证了智能缓存可提升可扩展性、内存效率，并支持边缘物联网网络中可靠运行，适用于智慧城市、医疗等对延迟敏感的应用场景。
原文摘要 · Abstract (English)
Federated Learning (FL) allows multiple distributed devices to jointly train a shared model without centralizing data, but communication cost remains a major bottleneck, especially in resource-constrained environments. This paper introduces caching strategies - FIFO, LRU, and Priority-Based - to reduce unnecessary model update transmissions. By selectively forwarding significant updates, our approach lowers bandwidth usage while maintaining model accuracy. Experiments on CIFAR-10 and medical datasets show reduced communication with minimal accuracy loss. Results confirm that intelligent caching improves scalability, memory efficiency, and supports reliable FL in edge IoT networks, making it practical for deployment in smart cities, healthcare, and other latency-sensitive applications.
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