Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments

TL;DR

This paper proposes caching strategies like FIFO, LRU, and Priority-Based to reduce communication costs in federated learning for IoT devices, maintaining accuracy while improving scalability and efficiency.

Contribution

It introduces novel caching techniques specifically designed for federated learning in resource-constrained IoT environments, enhancing communication efficiency.

Findings

Significant reduction in bandwidth usage.

Minimal impact on model accuracy.

Improved scalability and resource efficiency.

Abstract

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.