Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming
2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)(2023)
Abstract
Model reprogramming (MR) is an emerging and powerful technique that provides cross-domain machine learning by enabling a model that is well-trained on some source task to be used for a different target task without finetuning the model weights. In this work, we propose Reprogrammable-FL, the first framework adapting MR to the setting of differentially private federated learning (FL), and demonstrate that it significantly improves the utility-privacy tradeoff compared to standard transfer learning methods (full/partial finetuning) and training from scratch in FL. Experimental results on several deep neural networks and datasets show up to over 60% accuracy improvement given the same privacy budget. The code repository can be found at https://github.com/IBM/reprogrammble-FL.
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Key words
Model Reprogramming,Differential Privacy,Federated Learning,Privacy-Accuracy Tradeoff
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