As Federated Learning (FL) grows in popularity, new decentralized frameworks
are becoming widespread. These frameworks leverage the benefits of
decentralized environments to enable fast and energy-efficient inter-device
communication. However, this growing popularity also intensifies the need for
robust security measures. While existing research has explored various aspects
of FL security, the role of adversarial node placement in decentralized
networks remains largely unexplored. This paper addresses this gap by analyzing
the performance of decentralized FL for various adversarial placement
strategies when adversaries can jointly coordinate their placement within a
network. We establish two baseline strategies for placing adversarial node:
random placement and network centrality-based placement. Building on this
foundation, we propose a novel attack algorithm that prioritizes adversarial
spread over adversarial centrality by maximizing the average network distance
between adversaries. We show that the new attack algorithm significantly
impacts key performance metrics such as testing accuracy, outperforming the
baseline frameworks by between 9% and 66.5% for the considered setups. Our
findings provide valuable insights into the vulnerabilities of decentralized FL
systems, setting the stage for future research aimed at developing more secure
and robust decentralized FL frameworks.

通过分析分散式节点放置策略对分散式联合学习性能的影响，本文提出了一种新的攻击算法，该算法通过最大化敌对节点之间的平均网络距离，优先考虑对抗传播而非对抗中心性，显著影响测试准确率等关键性能指标，并为分散式联合学习系统的漏洞提供了有价值的见解，为今后开展更安全和稳健的分散式联合学习框架的研究奠定了基础。

去中心化联邦学习网络中对抗节点部署的影响

The Impact of Adversarial Node Placement in Decentralized Federated  Learning Networks

A machine learning configuration refers to a combination of preprocessor,
learner, and hyperparameters. Given a set of configurations and a large dataset
randomly split into training and testing set, we study how to efficiently
select the best configuration with approximately the highest testing accuracy
when trained from the training set. To guarantee small accuracy loss, we
develop a solution using confidence interval (CI)-based progressive sampling
and pruning strategy. Compared to using full data to find the exact best
configuration, our solution achieves more than two orders of magnitude speedup,
while the returned top configuration has identical or close test accuracy.

研究了在给定一组配置和一个大的数据集随机划分为训练和测试集的情况下，如何在训练集上训练以获取近似最高测试精度的最佳配置，使用置信区间（CI）-based 渐进采样和剪枝策略，能够保证小的精度损失。与使用完整数据寻找精确最佳配置相比，我们的解决方案实现了超过两个数量级的速度提升，而返回的顶部配置具有相同或接近的测试准确性。

ABC：在大规模数据集上高效选择机器学习配置

ABC: Efficient Selection of Machine Learning Configuration on Large  Dataset

We present observations and discussion of previously unreported phenomena
discovered while training residual networks. The goal of this work is to better
understand the nature of neural networks through the examination of these new
empirical results. These behaviors were identified through the application of
Cyclical Learning Rates (CLR) and linear network interpolation. Among these
behaviors are counterintuitive increases and decreases in training loss and
instances of rapid training. For example, we demonstrate how CLR can produce
greater testing accuracy than traditional training despite using large learning
rates. Files to replicate these results are available at
this https URL

通过观测神经网络的的新结果，通过应用循环学习速率（CLR）和线性网络插值，我们探讨了训练残差网络时发现的以前未报告的现象，其行为包括训练损失的反直觉增减和快速训练，如 CLR 即使使用较大的学习速率也能产生更高的测试准确性。