Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments
This work addresses communication efficiency for resource-constrained IoT devices in applications like smart cities and healthcare, but it is incremental as it applies known caching techniques to a specific FL bottleneck.
The paper tackled the communication cost bottleneck in Federated Learning for IoT environments by introducing caching strategies like FIFO, LRU, and Priority-Based to reduce unnecessary model update transmissions, resulting in reduced bandwidth usage with minimal accuracy loss on datasets such as CIFAR-10 and medical data.
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.