迈向学习增强的P2P网络：带有不可信建议的自稳定图线性化
Towards Learning-Augmented Peer-to-Peer Networks: Self-Stabilizing Graph Linearization with Untrusted Advice
AI总结：
针对P2P网络，提出结合不可信监督者建议的自稳定图线性化算法，在建议正确时实现$O(\log n)$恢复时间，错误时保持原性能，并给出匹配下界。
摘要：
分布式对等系统因其去中心化特性而广受欢迎，这确保了没有节点对系统功能至关重要。然而，完全去中心化的解决方案通常设计难度更大，且与节点连接到强大服务器的中心化方法相比，开销往往高得多。另一方面，中心化方法存在单点故障。那么，是否有办法结合两者的优势而不继承其劣势？为此，我们考虑了一种有监督的对等方法，节点可以向潜在不可靠的监督者请求建议。这符合日益流行的“带预测的算法”或“学习增强算法”范式，但我们是首个在对等网络背景下考虑该范式的研究。具体而言，我们针对分布式图线性化这一基本问题设计了自稳定算法，节点需在瞬态故障后从任意初始网络恢复出“有序线”网络。在监督者帮助下，若建议正确，节点可在$O(\log n)$时间内恢复有序线网络；否则，算法保持原有恢复时间（即无监督者时）。我们克服的关键挑战是正确组合多个自稳定算法：一个处理并利用建议，另一个完全不依赖建议。核心技术贡献结合了overlay networks和proof-labeling schemes领域的思想。最后，若建议可能被破坏，我们给出了任何算法恢复时间的匹配下界$\Omega(\log n)$，其中$n$为网络规模。
英文摘要：
Distributed peer-to-peer systems are widely popular due to their decentralized nature, which ensures that no peer is critical for the functionality of the system. However, fully decentralized solutions are usually much harder to design, and tend to have a much higher overhead compared to centralized approaches, where the peers are connected to a powerful server. On the other hand, centralized approaches have a single point of failure. Thus, is there some way to combine their advantages without inheriting their disadvantages? To that end, we consider a supervised peer-to-peer approach where the peers can ask a potentially unreliable supervisor for advice. This is in line with the increasingly popular algorithmic paradigm called algorithms with predictions or learning-augmented algorithms, but we are the first to consider it in the context of peer-to-peer networks. Specifically, we design self-stabilizing algorithms for the fundamental problem of distributed graph linearization, where peers are supposed to recover the "sorted line" network from any initial network after a transient fault. With the help of the supervisor, peers can recover the sorted line network in $O(\log n)$ time, if the advice is correct; otherwise, the algorithm retains its original recovery time (i.e., without any supervisor). A crucial challenge that we overcome is to correctly compose multiple self-stabilizing algorithms, that is, one that processes and exploits the advice, and another that does not rely on the advice at all. Our key technical contributions combine ideas from the fields of overlay networks and proof-labeling schemes. Finally, we give a matching lower bound of $Ω(\log n)$ for the recovery time of any algorithm if the advice can be corrupted, where $n$ is the network size.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
