Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It

TMLR Paper6735 Authors

Abstract:

The success of federated learning (FL) ultimately depends on how strategic participants behave
under partial observability, yet most formulations still treat FL as a static optimization
problem. We instead view FL deployments as governed strategic systems and develop an analytical
framework that separates welfare-improving behavior from metric gaming. Within
this framework, we introduce indices that quantify manipulability, the price of gaming, and
the price of cooperation, and we use them to study how rules, information disclosure, evaluation
metrics, and aggregator-switching policies reshape incentives and cooperation patterns.
We derive threshold conditions for deterring harmful gaming while preserving benign cooperation,
and for triggering auto-switch rules when early-warning indicators become critical.
Building on these results, we construct a design toolkit including a governance checklist and
a simple audit-budget allocation algorithm with a provable performance guarantee. Simulations
across diverse stylized environments and a federated learning case study consistently
match the qualitative and quantitative patterns predicted by our framework. Taken together,
our results provide design principles and operational guidelines for reducing metric
gaming while sustaining stable, high-welfare cooperation in FL platforms.

Submission Type: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Ian_A._Kash1

Submission Number: 6735