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Federated learning (FL) is a popular collaborative learning paradigm, whereby agents with individual datasets can jointly train an ML model. While higher data sharing improves model accuracy and leads to higher payoffs, it also raises costs associated with data acquisition or loss of privacy, causing agents to be strategic about their data contribution. This leads to undesirable behavior at a Nash equilibrium (NE) such as free-riding, resulting in sub-optimal fairness, data sharing, and welfare. To address this, we design MSHAP, a budget-balanced payment mechanism for FL, that admits Nash equilibria under mild conditions, and achieves reciprocal fairness: where each agent's payoff equals her contribution to the collaboration, as measured by the Shapley share. In addition to fairness, we show that the NE under MSHAP has desirable guarantees in terms of accuracy, welfare, and total data collected. We validate our theoretical results through experiments, demonstrating that MSHAP outperforms baselines in terms of fairness and efficiency.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00426-017-0848-8
