The increasing cloudification and softwarization of networks foster the
interplay among multiple independently managed deployments. An appealing reason
for such an interplay lies in distributed Machine Learning (ML), which allows
the creation of robust ML models by leveraging collective intelligence and
computational power. In this paper, we study the application of the two
cornerstones of distributed learning, namely Federated Learning (FL) and
Knowledge Distillation (KD), on the Wi-Fi Access Point (AP) load prediction use
case. The analysis conducted in this paper is done on a dataset that contains
real measurements from a large Wi-Fi campus network, which we use to train the
ML model under study based on different strategies. Performance evaluation
includes relevant aspects for the suitability of distributed learning operation
in real use cases, including the predictive performance, the associated
communication overheads, or the energy consumption. In particular, we prove
that distributed learning can improve the predictive accuracy centralized ML
solutions by up to 93% while reducing the communication overheads and the
energy cost by 80%.

通过研究分布式学习的两个基本方法 —— 联邦学习（FL）和知识蒸馏（KD）在 Wi-Fi 接入点（AP）负载预测用例上的应用，本文证明了分布式学习可以将集中式机器学习解决方案的预测准确性提高 93％，并将通信开销和能源成本降低 80％。

Wi-Fi AP 负载预测的分布式学习

Distributed Learning for Wi-Fi AP Load Prediction

Fifth generation (5G) cellular networks will serve a wide variety of
heterogeneous use cases, including mobile broadband users, ultra-low latency
services and massively dense connectivity scenarios. The resulting diverse
communication requirements will demand networking with unprecedented
flexibility, not currently provided by the monolithic black-box approach of 4G
cellular networks. The research community and an increasing number of
standardization bodies and industry coalitions have recognized softwarization,
virtualization, and disaggregation of networking functionalities as the key
enablers of the needed shift to flexibility. Particularly, software-defined
cellular networks are heralded as the prime technology to satisfy the new
application-driven traffic requirements and to support the highly time-varying
topology and interference dynamics, because of their openness through
well-defined interfaces, and programmability, for swift and responsive network
optimization. Leading the technological innovation in this direction, several
5G software-based projects and alliances have embraced the open source
approach, making new libraries and frameworks available to the wireless
community. This race to open source softwarization, however, has led to a
deluge of solutions whose interoperability and interactions are often unclear.
This article provides the first cohesive and exhaustive compendium of recent
open source software and frameworks for 5G cellular networks, with a full stack
and end-to-end perspective. We detail their capabilities and functionalities
focusing on how their constituting elements fit the 5G ecosystem, and unravel
the interactions among the surveyed solutions. Finally, we review hardware and
testbeds on which these frameworks can run, and discuss the limitations of the
state-of-the-art, as well as feasible directions toward fully open source,
programmable 5G networks.

本文提供最近 5G 蜂窝网络的开源软件和框架的全面指南，重点介绍它们如何适应 5G 生态系统，并揭示了调查解决方案之间的交互作用。