The state-of-the-art face recognition systems are typically trained on a
single computer, utilizing extensive image datasets collected from various
number of users. However, these datasets often contain sensitive personal
information that users may hesitate to disclose. To address potential privacy
concerns, we explore the application of federated learning, both with and
without secure aggregators, in the context of both supervised and unsupervised
face recognition systems. Federated learning facilitates the training of a
shared model without necessitating the sharing of individual private data,
achieving this by training models on decentralized edge devices housing the
data. In our proposed system, each edge device independently trains its own
model, which is subsequently transmitted either to a secure aggregator or
directly to the central server. To introduce diverse data without the need for
data transmission, we employ generative adversarial networks to generate
imposter data at the edge. Following this, the secure aggregator or central
server combines these individual models to construct a global model, which is
then relayed back to the edge devices. Experimental findings based on the
CelebA datasets reveal that employing federated learning in both supervised and
unsupervised face recognition systems offers dual benefits. Firstly, it
safeguards privacy since the original data remains on the edge devices.
Secondly, the experimental results demonstrate that the aggregated model yields
nearly identical performance compared to the individual models, particularly
when the federated model does not utilize a secure aggregator. Hence, our
results shed light on the practical challenges associated with
privacy-preserving face image training, particularly in terms of the balance
between privacy and accuracy.

通过使用联邦学习（包括有和无安全聚合器）在受监督和无监督的人脸识别系统中进行训练，我们的实验结果表明联邦学习既保护了隐私又实现了与单独模型几乎相同的性能。我们的研究工作揭示了隐私保护的人脸图像训练中的实际挑战，尤其注重隐私和准确性的平衡问题。