Abstract
SMSes are a great way to communicate via short text. But, some people take advantage of this service by spamming innocent people. Deep learning models need more SMS data to be adaptive and accurate. But, SMS data being sensitive, should not leave the device premises. Therefore, we have proposed a federated learning approach in our research study. Initially, a distilBERT model having validation accuracy of 98% is transported to mobile clients. Mobile clients train this local model via the SMSes received and send their local model weights to server for aggregation. The process is done iteratively making the model robust and resistant to latest spam techniques. Model prediction analysis is done at server side using global model to check which words in message influence spam and ham. On-device training experiment is conducted on a client and it is observed that the losses of the global model converge after every iteration.
J. Sidhpura, P. Shah, R. Veerkhare—Contributed equally to this work.
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Sidhpura, J., Shah, P., Veerkhare, R., Godbole, A. (2023). FedSpam: Privacy Preserving SMS Spam Prediction. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_5
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