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Variable step size LMS is generally used to improve speed of convergence or decrease steady state error. Leaky adaptation is used to combat problems like build uppotential instability of quantization errorsthe filter in a finite-precision implementation. It is closely related to L2 norm regularization technique and results in continuous down scaling of filter coefficients (hence smoothing the extracted filter). So comparing these two techniques is not that meaningful. In fact you can easily use both techniques together to achieve the above advantages.

Variable step size LMS is generally used to improve speed of convergence or decrease steady state error. Leaky adaptation is used to combat problems like build up of quantization errors. It is closely related to L2 norm regularization technique and results in continuous down scaling of filter coefficients. So comparing these two techniques is not that meaningful. In fact you can easily use both techniques together to achieve the above advantages.

Variable step size LMS is generally used to improve speed of convergence or decrease steady state error. Leaky adaptation is used to combat problems like potential instability of the filter in a finite-precision implementation. It is closely related to L2 norm regularization technique and results in continuous down scaling of filter coefficients (hence smoothing the extracted filter). So comparing these two techniques is not that meaningful. In fact you can easily use both techniques together to achieve the above advantages.

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Variable step size LMS is generally used to improve speed of convergence or decrease steady state error. Leaky adaptation is used to combat problems like build up of quantization errors. It is closely related to L2 norm regularization technique and results in continuous down scaling of filter coefficients. So comparing these two techniques is not that meaningful. In fact you can easily use both techniques together to achieve the above advantages.