Abstract
The Internet of Things (IoT) is rapidly transforming industries by enabling seamless data collection and processing. However, the massive influx of data poses significant challenges in terms of energy consumption and privacy. Federated Learning (FL) has emerged as a promising solution, allowing distributed model training without transmitting raw data. This research proposes an Enhanced Federated Learning Framework (EFLF) for edge-enabled green IoT that optimizes energy efficiency while maintaining high model accuracy. The proposed framework integrates adaptive client selection, energy-aware aggregation, and model compression techniques. Experimental results demonstrate superior performance in terms of energy efficiency and model convergence compared to baseline FL approaches.
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