Web: http://arxiv.org/abs/2201.08786

Jan. 24, 2022, 2:10 a.m. | Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, Luigi V. Mancini

cs.LG updates on arXiv.org arxiv.org

Proposed as a solution to mitigate the privacy implications related to the
adoption of deep learning solutions, Federated Learning (FL) enables large
numbers of participants to successfully train deep neural networks without
having to reveal the actual private training data. To date, a substantial
amount of research has investigated the security and privacy properties of FL,
resulting in a plethora of innovative attack and defense strategies. This paper
thoroughly investigates the communication capabilities of an FL scheme. In
particular, we …

arxiv communication federated learning learning

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