Developed a privacy-preserving federated learning framework that replaces weight-sharing with loss-based mutual learning, reducing bandwidth usage and model inversion attack risks. By leveraging knowledge distillation and deep mutual learning, clients share insights without exposing sensitive data, improving model generalization. The framework was evaluated on a face mask detection case study, demonstrating superior performance compared to traditional synchronous and asynchronous federated learning methods.

Developed an image compression tool in MATLAB using DFT, FFT, and DCT, implementing algorithms from scratch. The project optimized Fourier-based compression, benchmarked it against JPEG, and integrated a GUI for real-time visualization. Key concepts include Fourier Transform for frequency-domain compression, matrix transformations and quantization for data reduction, benchmarking compression efficiency across techniques, and a graphical user interface for user-controlled compression.