Traditional federated learning mainly focuses on parallel settings (PFL),
which can suffer significant communication and computation costs. In contrast,
one-shot and sequential federated learning (SFL) have emerged as innovative
paradigms to alleviate these costs. However, the issue of non-IID (Independent
and Identically Distributed) data persists as a significant challenge in
one-shot and SFL settings, exacerbated by the restricted communication between
clients. In this paper, we improve the one-shot sequential federated learning
for non-IID data by proposing a local model diversity-enhancing strategy.
Specifically, to leverage the potential of local model diversity for improving
model performance, we introduce a local model pool for each client that
comprises diverse models generated during local training, and propose two
distance measurements to further enhance the model diversity and mitigate the
effect of non-IID data. Consequently, our proposed framework can improve the
global model performance while maintaining low communication costs. Extensive
experiments demonstrate that our method exhibits superior performance to
existing one-shot PFL methods and achieves better accuracy compared with
state-of-the-art one-shot SFL methods on both label-skew and domain-shift tasks
(e.g., 6%+ accuracy improvement on the CIFAR-10 dataset).

通过提出一种局部模型多样性增强策略，我们改进了非独立同分布（non-IID）数据下的一次性序列联邦学习，从而提高了全局模型的性能并保持低通信成本。

增强本地模型多样性的非独立同分布数据的一次性连续联邦学习

One-Shot Sequential Federated Learning for Non-IID Data by Enhancing  Local Model Diversity

3D facial avatar reconstruction has been a significant research topic in
computer graphics and computer vision, where photo-realistic rendering and
flexible controls over poses and expressions are necessary for many related
applications. Recently, its performance has been greatly improved with the
development of neural radiance fields (NeRF). However, most existing NeRF-based
facial avatars focus on subject-specific reconstruction and reenactment,
requiring multi-shot images containing different views of the specific subject
for training, and the learned model cannot generalize to new identities,
limiting its further applications. In this work, we propose a one-shot 3D
facial avatar reconstruction framework that only requires a single source image
to reconstruct a high-fidelity 3D facial avatar. For the challenges of lacking
generalization ability and missing multi-view information, we leverage the
generative prior of 3D GAN and develop an efficient encoder-decoder network to
reconstruct the canonical neural volume of the source image, and further
propose a compensation network to complement facial details. To enable
fine-grained control over facial dynamics, we propose a deformation field to
warp the canonical volume into driven expressions. Through extensive
experimental comparisons, we achieve superior synthesis results compared to
several state-of-the-art methods.

提出了一种只需要一张源图像就能重建高保真度的三维面部角色建模方法，通过使用 3D GAN 的生成先验和有效的编码器 - 解码器网络来重建源图像的规范神经体积，同时使用补偿网络来补充面部细节，并引入变形场来对面部表情进行精细控制。与多种先进方法相比，实验结果显示合成效果更加优越。

NOFA：基于 NeRF 的一次性面部头像重建

NOFA: NeRF-based One-shot Facial Avatar Reconstruction

Neural architecture search (NAS) is an approach for automatically designing a
neural network architecture without human effort or expert knowledge. However,
the high computational cost of NAS limits its use in commercial applications.
Two recent NAS paradigms, namely one-shot and sparse propagation, which reduce
the time and space complexities, respectively, provide clues for solving this
problem. In this paper, we propose a novel search strategy for one-shot and
sparse propagation NAS, namely AdvantageNAS, which further reduces the time
complexity of NAS by reducing the number of search iterations. AdvantageNAS is
a gradient-based approach that improves the search efficiency by introducing
credit assignment in gradient estimation for architecture updates. Experiments
on the NAS-Bench-201 and PTB dataset show that AdvantageNAS discovers an
architecture with higher performance under a limited time budget compared to
existing sparse propagation NAS. To further reveal the reliabilities of
AdvantageNAS, we investigate it theoretically and find that it monotonically
improves the expected loss and thus converges.

本文介绍了一种基于梯度下降的神经结构搜索方法，AdvantageNAS，它通过引入梯度传递中的信用分配来提高搜索效率，减少搜索迭代次数，进一步减少 NAS 的时间复杂度，实验结果表明，在给定时间预算下，AdvantageNAS 可以发现性能更高的神经网络结构。