Optimization-based techniques for federated learning (FL) often come with
prohibitive communication cost, as high dimensional model parameters need to be
communicated repeatedly between server and clients. In this paper, we follow a
Bayesian approach allowing to perform FL with one-shot communication, by
solving the global inference problem as a product of local client posteriors.
For models with multi-modal likelihoods, such as neural networks, a naive
application of this scheme is hampered, since clients will capture different
posterior modes, causing a destructive collapse of the posterior on the server
side. Consequently, we explore approximate inference in the function-space
representation of client posteriors, hence suffering less or not at all from
multi-modality. We show that distributed function-space inference is tightly
related to learning Bayesian pseudocoresets and develop a tractable Bayesian FL
algorithm on this insight. We show that this approach achieves prediction
performance competitive to state-of-the-art while showing a striking reduction
in communication cost of up to two orders of magnitude. Moreover, due to its
Bayesian nature, our method also delivers well-calibrated uncertainty
estimates.

基于优化的技术在联邦学习中通常具有高昂的通信成本，本文提出了一种基于贝叶斯方法的联邦学习算法，通过分布函数空间推断实现了仅需一次通信，并且在多模态条件下能够更好地进行近似推断，从而大大降低了通信成本。此算法在预测性能方面与最新技术水平相当，并能提供可靠的不确定性估计。

基于贝叶斯伪核心集的一次性联邦学习

One-Shot Federated Learning with Bayesian Pseudocoresets

In this work, we consider the complex control problem of making a monopod
reach a target with a jump. The monopod can jump in any direction and the
terrain underneath its foot can be uneven. This is a template of a much larger
class of problems, which are extremely challenging and computationally
expensive to solve using standard optimisation-based techniques. Reinforcement
Learning (RL) could be an interesting alternative, but the application of an
end-to-end approach in which the controller must learn everything from scratch,
is impractical. The solution advocated in this paper is to guide the learning
process within an RL framework by injecting physical knowledge. This expedient
brings to widespread benefits, such as a drastic reduction of the learning
time, and the ability to learn and compensate for possible errors in the
low-level controller executing the motion. We demonstrate the advantage of our
approach with respect to both optimization-based and end-to-end RL approaches.

该论文提出了一种利用强化学习框架中注入物理知识来引导学习过程的解决方案，旨在解决一类复杂控制问题，例如单脚架在任意方向上跳到目标位置的问题，以及在不平坦的地形上的运动，表明该方法相对于基于优化和端到端强化学习方法具有明显优势。

高效的跳跃式单脚支架强化学习

Efficient Reinforcement Learning for Jumping Monopods

Graph-based semi-supervised learning is the problem of propagating labels
from a small number of labelled data points to a larger set of unlabelled data.
This paper is concerned with the consistency of optimization-based techniques
for such problems, in the limit where the labels have small noise and the
underlying unlabelled data is well clustered. We study graph-based probit for
binary classification, and a natural generalization of this method to
multi-class classification using one-hot encoding. The resulting objective
function to be optimized comprises the sum of a quadratic form defined through
a rational function of the graph Laplacian, involving only the unlabelled data,
and a fidelity term involving only the labelled data. The consistency analysis
sheds light on the choice of the rational function defining the optimization.

本文研究了基于图的半监督学习问题，探讨了标签带有小噪声和无标签数据集有良好聚类性质时，优化技术的一致性，分析了基于图的 Probit 算法和应用 One-Hot 编码的多类分类方法的优化目标函数，通过一致性分析阐明了定义优化所需的理性函数选择。