PFL-LDG: Privacy-preserving Federated Learning via Lightweight Device Grouping
Zhilu Wang,
Qi Liu,
Xiaodong Liu
Abstract:The rapid growth of private data from distributed edge networks, driven by the proliferation of IoT sensors, wearable devices, and smartphones, offers significant opportunities for AI applications. However, traditional distributed machine learning methods struggle to address data privacy concerns effectively. Federated learning (FL) has appeared as a popular, innovative paradigm for distributed machine learning that enables collaborative training of models across multiple data silos while preserving privacy. Y… Show more
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