Towards General Deep Leakage in Federated Learning. (arXiv:2110.09074v2 [cs.LG] UPDATED)

Unlike traditional central training, federated learning (FL) improves the
performance of the global model by sharing and aggregating local models rather
than local data to protect the users' privacy. Although this training approach
appears secure, some research has demonstrated that an attacker can still
recover private data based on the shared gradient information. This on-the-fly
reconstruction attack deserves to be studied in depth because it can occur at
any stage of training, whether at the beginning or at the end …

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