Distributed machine learning approaches, including a broad class of federated
learning techniques, present a number of benefits when deploying machine
learning applications over widely distributed infrastructures. To realize the
expected benefits, however, introduces substantial operational challenges due
to required application and configuration-level changes related to
deployment-specific details. Such complexities can be greatly reduced by
introducing higher-level abstractions -- role and channel -- using which
federated learning applications are described as Topology Abstraction Graphs
(TAGs). TAGs decouple the ML application logic from the underlying deployment
details, making it possible to specialize the application deployment, thus
reducing development effort and paving the way for improved automation and
tuning. We present Flame, the first system that supports these abstractions,
and demonstrate its benefits for several use cases.

使用拓扑抽象图标识分布式机器学习应用，可避免部署细节差异带来的复杂性，提高系统自动化和优化效率，Flame 是第一个支持该抽象的系统。

Flame 简化联邦学习操作

Federated Learning Operations Made Simple with Flame

Emerging research frontiers and computational advances have gradually
transformed cognitive science into a multidisciplinary and data-driven field.
As a result, there is a proliferation of cognitive theories investigated and
interpreted from different academic lens and in different levels of
abstraction. We formulate this applied aspect of this challenge as the
computational cognitive inference, and describe the major routes of
computational approaches. To balance the potential optimism alongside the speed
and scale of the data-driven era of cognitive science, we propose to inspect
this trend in more empirical terms by identifying the operational challenges,
societal impacts and ethical guidelines in conducting research and interpreting
results from the computational inference in cognitive science.

通过识别认知科学中计算推理的计算方法、数据驱动时代的操作挑战、社会影响和道德准则等方面，平衡潜在的乐观情绪，探索在更实证的视角下进行推理的可行性和可行性，以更多样化和丰富的方法解释研究结果。