Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach

TL;DR

This paper introduces a multi-agent reinforcement learning-based adaptive incentive mechanism for cross-silo federated learning, effectively encouraging organizations to contribute data by considering environmental dynamics and without requiring private information.

Contribution

It proposes a novel adaptive incentive mechanism using multi-agent reinforcement learning that accounts for environmental dynamics and does not rely on organizations sharing private data.

Findings

Achieves adaptive incentives for organizations in cross-silo FL

Improves long-term payoffs for participating organizations

Effectively handles environmental uncertainties in training

Abstract

Cross-silo federated learning (FL) is a typical FL that enables organizations(e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is key to encouraging organizations to contribute data. However, existing works on incentivizing cross-silo FL lack consideration of the environmental dynamics (e.g., precision of the trained global model and data owned by uncertain clients during the training processes). Moreover, most of them assume that organizations share private information, which is unrealistic. To overcome these limitations, we propose a novel adaptive mechanism for cross-silo FL, towards incentivizing organizations to contribute data to maximize their long-term payoffs in a real dynamic training environment. The mechanism is based on multi-agent reinforcement learning, which learns near-optimal data contribution strategy from the history of potential games without organizations' private information. Experiments demonstrate that our mechanism achieves adaptive incentive and effectively improves the long-term payoffs for organizations.