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SOHO-FL: A Fast Reconvergent Intra-domain Routing Scheme Using Federated Learning

IEEE NETWORK(2024)

Beijing Univ Posts & Telecommun BUPT | Tsinghua Univ

Cited 2|Views38
Abstract
The development of machine learning provides a new paradigm for network optimizations, e.g., reinforcement learning (RL) has brought great improvements in many fields, such as adaptive video streaming, congestion control of TCP. The fundamental mechanism of such RL-based architectures is that the neural network decision model converges to a stable state by continuously interacting with network environment. However, for network routing problem, such RL-based strategies do not work well due to topology change. This is because topological changes would require the existing RL models to be retrained, while these models may stop making routing decisions or provide non-optimal decisions during the slow reconverging process of retraining, seriously affected transmission performance. To solve this problem, we proposed a fast convergent RL-model (SOHOFL), which can alleviate the performance degradation caused by the slow retraining process by federated learning. The experimental results based on real-world network topologies demonstrate that SOHO-FL outperforms the state-of-the-art algorithms in reconvergence time by 22.3% on average.
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Key words
Routing,Hidden Markov models,Data models,Computational modeling,Network topology,Optimization,Federated learning
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要点】:论文提出了一种名为SOHO-FL的快速重收敛内域路由方案,使用联邦学习来解决基于强化学习的路由策略因网络拓扑变化而效果不佳的问题,实验结果显示,在真实网络拓扑下,SOHO-FL的平均重收敛时间比现有算法快22.3%。

方法】:SOHO-FL利用联邦学习,通过跨多个网络节点分散训练模型并共享更新,以实现快速重收敛。

实验】:研究者在真实世界网络拓扑上进行了实验,比较了SOHO-FL与现有算法的性能,所使用的数据集未具体提及,实验结果显示SOHO-FL在重收敛时间上显著优于现有算法。