Federated learning (FL), a novel branch of distributed machine learning (ML),
develops global models through a private procedure without direct access to
local datasets. However, it is still possible to access the model updates
(gradient updates of deep neural networks) transferred between clients and
servers, potentially revealing sensitive local information to adversaries using
model inversion attacks. Differential privacy (DP) offers a promising approach
to addressing this issue by adding noise to the parameters. On the other hand,
heterogeneities in data structure, storage, communication, and computational
capabilities of devices can cause convergence problems and delays in developing
the global model. A personalized weighted averaging of local parameters based
on the resources of each device can yield a better aggregated model in each
round. In this paper, to efficiently preserve privacy, we propose a
personalized DP framework that injects noise based on clients' relative impact
factors and aggregates parameters while considering heterogeneities and
adjusting properties. To fulfill the DP requirements, we first analyze the
convergence boundary of the FL algorithm when impact factors are personalized
and fixed throughout the learning process. We then further study the
convergence property considering time-varying (adaptive) impact factors.

通过个性化差分隐私框架和考虑各种异构性和属性调整的方法，该论文提出了一种高效地保护隐私的个性化差分隐私框架，并对联邦学习算法的收敛边界进行了分析，同时进一步研究了考虑时间变化的个性化因素的收敛性质。

运用自适应差分隐私和基于优先级的聚合增强联邦学习

Enhancing Federated Learning with Adaptive Differential Privacy and  Priority-Based Aggregation

Despite the success of neural networks (NNs), there is still a concern among
many over their "black box" nature. Why do they work? Here we present a simple
analytic argument that NNs are in fact essentially polynomial regression
models. This view will have various implications for NNs, e.g. providing an
explanation for why convergence problems arise in NNs, and it gives rough
guidance on avoiding overfitting. In addition, we use this phenomenon to
predict and confirm a multicollinearity property of NNs not previously reported
in the literature. Most importantly, given this loose correspondence, one may
choose to routinely use polynomial models instead of NNs, thus avoiding some
major problems of the latter, such as having to set many tuning parameters and
dealing with convergence issues. We present a number of empirical results; in
each case, the accuracy of the polynomial approach matches or exceeds that of
NN approaches. A many-featured, open-source software package, polyreg, is
available.

神经网络与多项式回归模型之间有着松散的对应关系，通过揭示这一关系，我们可以在避免诸如设置大量调整参数和处理收敛问题等神经网络的重要问题的同时，选择使用多项式模型。