Federated Learning (FL) enables collaborative model building among a large
number of participants without the need for explicit data sharing. But this
approach shows vulnerabilities when privacy inference attacks are applied to
it. In particular, in the event of a gradient leakage attack, which has a
higher success rate in retrieving sensitive data from the model gradients, FL
models are at higher risk due to the presence of communication in their
inherent architecture. The most alarming thing about this gradient leakage
attack is that it can be performed in such a covert way that it does not hamper
the training performance while the attackers backtrack from the gradients to
get information about the raw data. Two of the most common approaches proposed
as solutions to this issue are homomorphic encryption and adding noise with
differential privacy parameters. These two approaches suffer from two major
drawbacks. They are: the key generation process becomes tedious with the
increasing number of clients, and noise-based differential privacy suffers from
a significant drop in global model accuracy. As a countermeasure, we propose a
mixed-precision quantized FL scheme, and we empirically show that both of the
issues addressed above can be resolved. In addition, our approach can ensure
more robustness as different layers of the deep model are quantized with
different precision and quantization modes. We empirically proved the validity
of our method with three benchmark datasets and found a minimal accuracy drop
in the global model after applying quantization.

研究提出了混合精度量化 FL 方案作为反制措施，该方案可解决加密密钥的生成过程由于客户端数量增加而变得繁琐的难题，同时通过量化不同精度不同模式中深度模型的不同层，使该方案具有更多的鲁棒性，实证分析证明，应用量化后全局模型的准确性仅有轻微下降。