We propose the first parameter-free decentralized online learning algorithms with network regret guarantees, which achieve sublinear regret without requiring hyperparameter tuning. This family of algorithms connects multi-agent coin-betting and decentralized online learning via gossip steps. To enable our decentralized analysis, we introduce a novel "betting function" formulation for coin-betting that simplifies the multi-agent regret analysis. Our analysis shows sublinear network regret bounds and is validated through experiments on synthetic and real datasets. This family of algorithms is applicable to distributed sensing, decentralized optimization, and collaborative ML applications.

翻译：我们提出了首个具有网络遗憾保证的无参数去中心化在线学习算法，该算法无需超参数调优即可实现次线性遗憾。该算法族通过通信步骤将多智能体投币博弈与去中心化在线学习相连接。为实现去中心化分析，我们为投币博弈引入了一种新颖的"投注函数"表述，从而简化了多智能体遗憾分析。我们的分析展示了次线性网络遗憾界限，并通过合成与真实数据集的实验验证了其有效性。该算法族适用于分布式感知、去中心化优化及协作式机器学习等应用场景。