Fair yet Asymptotically Equal Collaborative Learning
Xiaoqiang Lin Xinyi Xu See-Kiong Ng Chuan-Sheng Foo Bryan Kian Hsiang Low
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.