[2509.02391] Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It

Computer Science > Machine Learning arXiv:2509.02391 (cs) [Submitted on 2 Sep 2025 (v1), last revised 2 Mar 2026 (this version, v3)] Title:Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It Authors:Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool, Gisung Oh View a PDF of the paper titled Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It, by Dongseok Kim and 3 other authors View PDF HTML (experimental) 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 ...