In Online Learning to Rank (OLTR), ranking models are trained directly from live user interactions, but existing systems rely on a trusted central server to collect and process these interactions. This leaves operators free to introduce biases that conflict with user interests. Decentralized learning offers an attractive alternative, allowing users to collaboratively train a shared ranking model by exchanging model updates directly with one another, without any central authority. In such settings, however, malicious nodes can send poisoned model updates that degrade the ranking quality of honest nodes. We introduce RankGuard, a decentralized OLTR framework in which users collaboratively train ranking models and exchange model updates directly with other nodes. RankGuard defends against poisoning attacks by carefully evaluating incoming models against the user's own private click history, corrected for position bias. An incoming model is only aggregated if it better explains the user's past interactions than the current local model, making it fundamentally hard for malicious nodes to craft updates that pass this test without also genuinely helping the user. We derive a theoretical convergence guarantee of RankGuard. To the best of our knowledge, this is the first formal convergence analysis of a decentralized OLTR algorithm. We evaluate RankGuard against four poisoning attacks, including a powerful adaptive attack, using four standard benchmarks and three click models. RankGuard outperforms all baselines in most settings while being up to 62x more efficient than its closest competitors.

翻译：在线学习排序（OLTR）中，排序模型直接从真实用户交互中训练，但现有系统依赖可信中央服务器收集和处理这些交互，这可能导致运营商随意引入与用户利益相悖的偏差。去中心化学习提供了一种理想的替代方案：用户无需中央权威机构即可通过直接交换模型更新来协作训练共享排序模型。然而在此类设置中，恶意节点可能发送毒化模型更新，损害诚实节点的排序质量。我们提出RankGuard——一种去中心化OLTR框架，用户可协作训练排序模型并直接与其他节点交换模型更新。RankGuard通过结合位置偏差校正，将传入模型与用户私有点击历史进行审慎评估来防御毒化攻击：仅当传入模型比当前本地模型更优解释用户历史交互时才被聚合，这使得恶意节点几乎无法在不真正帮助用户的情况下构造通过检测的更新。我们推导了RankGuard的理论收敛保证——据我们所知，这是首个针对去中心化OLTR算法的形式化收敛性分析。使用四个标准基准测试和三种点击模型，我们针对包括强适应性攻击在内的四种毒化攻击评估了RankGuard。在多数场景中，RankGuard表现优于所有基线方法，且效率较最接近的竞争对手提升高达62倍。