Güner Dilsad Er

This work presents Co-VLA, which applies consensus optimization using the Alternating Direction Method of Multipliers~(ADMM) to federated VLA training, and demonstrates that Co-VLA achieves performance comparable to centralized training in both full-model training and parameter-efficient fine-tuning settings.