Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It
This addresses the challenge of ensuring cooperation and preventing gaming in Federated Learning systems, which is crucial for deploying reliable and fair AI models in distributed settings, though it is incremental in building on existing incentive and monitoring approaches.
The paper tackles the problem of strategic behavior and metric gaming in Federated Learning by modeling it as a strategic system, introducing indices to quantify incentives and performance loss, and providing practical tools like thresholds and audit algorithms to monitor and mitigate these issues. Simulations validate the framework, showing consistent patterns across diverse environments.
The success of Federated Learning depends on the actions that participants take out of sight. We model Federated Learning not as a mere optimization task but as a strategic system entangled with rules and incentives. From this perspective, we present an analytical framework that makes it possible to clearly identify where behaviors that genuinely improve performance diverge from those that merely target metrics. We introduce two indices that respectively quantify behavioral incentives and collective performance loss, and we use them as the basis for consistently interpreting the impact of operational choices such as rule design, the level of information disclosure, evaluation methods, and aggregator switching. We further summarize thresholds, auto-switch rules, and early warning signals into a checklist that can be applied directly in practice, and we provide both a practical algorithm for allocating limited audit resources and a performance guarantee. Simulations conducted across diverse environments consistently validate the patterns predicted by our framework, and we release all procedures for full reproducibility. While our approach operates most strongly under several assumptions, combining periodic recalibration, randomization, and connectivity-based alarms enables robust application under the variability of real-world operations. We present both design principles and operational guidelines that lower the incentives for metric gaming while sustaining and expanding stable cooperation.