Towards Learning-Augmented Peer-to-Peer Networks: Self-Stabilizing Graph Linearization with Untrusted Advice

2025-04-03

AI Summary

In distributed P2P networks, existing approaches struggle to simultaneously achieve high recovery efficiency and robustness after transient failures when restoring sorted linear topologies. Method: This paper proposes a learning-augmented self-stabilizing graph linearization method. Nodes dynamically query an unreliable supervisor for topology suggestions; if suggestions are trustworthy, recovery achieves the optimal O(log n) time; if suggestions are corrupted, the system gracefully degrades to conventional self-stabilization, preserving robustness. Contributions: (i) The first composable dual-mode self-stabilizing mechanism, pioneering the integration of learning-augmented paradigms into P2P self-stabilization; (ii) A novel architecture unifying overlay network design, proof-carrying labels, and suggestion verification to enable dynamic trust assessment and topology reconstruction; (iii) The first Ω(log n) lower bound on recovery time under adversarial suggestion corruption, establishing theoretical optimality of the proposed scheme.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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 $Omega(log n)$ for the recovery time of any algorithm if the advice can be corrupted, where $n$ is the network size.

Problem

• Combining decentralized and centralized P2P advantages without single-point failure
• Designing self-stabilizing graph linearization with unreliable supervisor advice
• Achieving logarithmic recovery time with fallback to original performance

Innovation

• Combines decentralized and supervised peer-to-peer approaches
• Uses self-stabilizing algorithms for graph linearization
• Ensures recovery time O(log n) with correct advice