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Feedback control in hearing aids mitigates acoustic feedback caused by the coupling between the receiver and microphone. While DNN-based methods have achieved progress, they remain computationally intensive with relatively high latency. This paper introduces L3C-DeepMFC, a low-latency and low-complexity time-frequency (T-F) domain method that employs complex spectrum mapping to estimate the magnitude and phase components of the desired speech. This method integrates full- and sub-band recurrent modeling to capture spectro-temporal patterns and modifies the overlap-add method for low-latency processing. Moreover, we utilize closed-loop fine tuning with dynamically generated feedback mixtures to minimize the mismatch between training and estimation. Evaluations using the AISHELL-3 dataset confirm its competitive performance across various gains, significantly improving the maximum stable gain (MSG). Integration with traditional methods shows better performance of feedback suppression.