Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning

Autor:	Kim, Joon, Park, Sejin
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	Federated Learning(FL), in theory, preserves privacy of individual clients' data while producing quality machine learning models. However, attacks such as Deep Leakage from Gradients(DLG) severely question the practicality of FL. In this paper, we empirically evaluate the efficacy of four defensive methods against DLG: Masking, Clipping, Pruning, and Noising. Masking, while only previously studied as a way to compress information during parameter transfer, shows surprisingly robust defensive utility when compared to the other three established methods. Our experimentation is two-fold. We first evaluate the minimum hyperparameter threshold for each method across MNIST, CIFAR-10, and lfw datasets. Then, we train FL clients with each method and their minimum threshold values to investigate the trade-off between DLG defense and training performance. Results reveal that Masking and Clipping show near to none degradation in performance while obfuscating enough information to effectively defend against DLG. Comment: 13 pages, 5 figures, to be submitted to Applied Intelligence
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.08430 Zobrazit plný text záznamu View this record from Arxiv