In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data. For the more resourceful nodes to be willing to share their model updates, they need to be fairly incentivized. This paper explores an incentive design that guarantees fairness so that nodes receive rewards commensurate to their contributions. Our approach leverages an explore-then-exploit formulation to estimate the nodes' contributions (i.e., exploration) for realizing our theoretically guaranteed fair incentives (i.e., exploitation). However, we observe a "rich get richer" phenomenon arising from the existing approaches to guarantee fairness and it discourages the participation of the less resourceful nodes. To remedy this, we additionally preserve asymptotic equality, i.e., less resourceful nodes achieve equal performance eventually to the more resourceful/"rich" nodes. We empirically demonstrate in two settings with real-world streaming data: federated online incremental learning and federated reinforcement learning, that our proposed approach outperforms existing baselines in fairness and learning performance while remaining competitive in preserving equality.

翻译：在流式数据的协同学习中，节点（如组织）通过共享基于其最新流式数据计算出的最新模型更新，共同且持续地学习一个机器学习模型。为了让资源更丰富的节点愿意分享其模型更新，需要给予它们公平的激励。本文探讨了一种保证公平性的激励设计，使节点能获得与其贡献相称的奖励。我们的方法利用“先探索后利用”的框架来估计节点贡献（即探索阶段），以实现理论上保证的公平激励（即利用阶段）。然而，我们发现现有保证公平性的方法会导致“富者愈富”现象，这阻碍了资源较少节点的参与。为解决此问题，我们额外保留了渐近平等性，即资源较少的节点最终能达到与资源更丰富的“富裕”节点同等的性能。我们在两个基于真实世界流式数据的场景中（联邦在线增量学习和联邦强化学习）进行了实证验证，结果表明我们所提出的方法在公平性和学习性能上均优于现有基线，同时在保持平等性方面也具有竞争力。