Performance Comparison of Quantized and Full-Precision SLMs in Heterogeneous Federated NLP

Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy,unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance ener

Research goal: How does the performance of 1B-10B parameter quantized SLMs compare to their full-precision counterparts across NLP tasks in federated learning setups under heterogeneous edge device conditions?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.