Machine Unlearning for Uplink Interference Cancellation
Machine unlearning (MUL) is introduced as a means to achieve interference cancellation within artificial intelligence (AI)-enabled wireless systems. It is observed that interference cancellation with MUL demonstrates $30\%$ improvement in a classification task accuracy in the presence of a corrupted...
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Zusammenfassung: | Machine unlearning (MUL) is introduced as a means to achieve interference
cancellation within artificial intelligence (AI)-enabled wireless systems. It
is observed that interference cancellation with MUL demonstrates $30\%$
improvement in a classification task accuracy in the presence of a corrupted AI
model. Accordingly, the necessity for instantaneous channel state information
for existing interference source is eliminated and a corrupted latent space
with interference noise is cleansed with MUL algorithm, achieving this without
the necessity for either retraining or dataset cleansing. A Membership
Inference Attack (MIA) served as a benchmark for assessing the efficacy of MUL
in mitigating interference within a neural network model. The advantage of the
MUL algorithm was determined by evaluating both the probability of interference
and the quantity of samples requiring retraining. In a simple signal-to-noise
ratio classification task, the comprehensive improvement across various test
cases in terms of accuracy demonstrates that MUL exhibits extensive
capabilities and limitations, particularly in native AI applications. |
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DOI: | 10.48550/arxiv.2406.05945 |