Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
Leon Witt, Togrul Abbasli, Kentaroh Toyoda, Wojciech Samek, Lucy Klinger
cs.LG cs.AI cs.GT
Abstract
We introduce Knowledge-Free Correlated Agreement (KFCA) to reward client contributions in federated learning (FL) without relying on ground truth, a public test set, or distribution knowledge. Under categorical reports and an honest majority, KFCA is strictly truthful, addressing the label-flipping vulnerability of Correlated Agreement (CA). We evaluate KFCA on federated LLM adapter tuning and a real-world PCB inspection task, showing efficient real-time reward computation suitable for decentralized and blockchain-based incentive designs.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.